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the final version

main
jzq 2024-06-21 15:44:42 +08:00
commit c7aeda2a41
341 changed files with 1219587 additions and 0 deletions
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149
CMakeLists.txt Normal file
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cmake_minimum_required(VERSION 2.8)
project(ORB_SLAM2)
add_definitions(-w)
IF(NOT CMAKE_BUILD_TYPE)
SET(CMAKE_BUILD_TYPE Release)
ENDIF()
MESSAGE("Build type: " ${CMAKE_BUILD_TYPE})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -O3 -march=native ")
set(CMAKE_CXX_FLAGS "-g -O3 -march=native") # 设置调试信息
#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -march=native")
# set(PCL_DIR "/home/hong/Desktop/pcl-pcl-1.9.1/cmake")
FIND_PACKAGE( OpenMP REQUIRED)
if(OPENMP_FOUND)
message("OPENMP FOUND")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()
# Check C++11 or C++0x support
include(CheckCXXCompilerFlag)
CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
CHECK_CXX_COMPILER_FLAG("-std=c++0x" COMPILER_SUPPORTS_CXX0X)
if(COMPILER_SUPPORTS_CXX11)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
add_definitions(-DCOMPILEDWITHC11)
message(STATUS "Using flag -std=c++11.")
elseif(COMPILER_SUPPORTS_CXX0X)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x")
add_definitions(-DCOMPILEDWITHC0X)
message(STATUS "Using flag -std=c++0x.")
else()
message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} has no C++11 support. Please use a different C++ compiler.")
endif()
LIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules)
find_package(OpenCV 3.0 QUIET)
if(NOT OpenCV_FOUND)
find_package(OpenCV 2.4.3 QUIET)
if(NOT OpenCV_FOUND)
message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
endif()
endif()
find_package(OpenGL REQUIRED)
find_package(GLUT REQUIRED)
find_package(Eigen3 3.1.0 REQUIRED)
find_package(Pangolin REQUIRED)
find_package(PCL REQUIRED)
include_directories(${OPENGL_INCLUDE_DIRS})
include_directories(${GLUT_INCLUDE_DIRS})
include_directories(
${PROJECT_SOURCE_DIR}
${PROJECT_SOURCE_DIR}/include
${PCL_INCLUDE_DIRS}
${EIGEN3_INCLUDE_DIR}
${Pangolin_INCLUDE_DIRS}
)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/lib)
add_library(${PROJECT_NAME} SHARED
src/System.cc
src/Tracking.cc
src/LocalMapping.cc
src/LoopClosing.cc
src/ORBextractor.cc
src/ORBmatcher.cc
src/FrameDrawer.cc
src/Converter.cc
src/MapPoint.cc
src/KeyFrame.cc
src/Map.cc
src/MapDrawer.cc
src/Optimizer.cc
src/PnPsolver.cc
src/Frame.cc
src/KeyFrameDatabase.cc
src/Sim3Solver.cc
src/Initializer.cc
src/Viewer.cc
src/Car.cpp
src/Semantic_Map.cc
)
target_link_libraries(${PROJECT_NAME}
${OpenCV_LIBS}
${EIGEN3_LIBS}
${OPENGL_LIBRARIES}
${GLUT_LIBRARIES}
${PCL_LIBRARIES}
${Pangolin_LIBRARIES}
${PROJECT_SOURCE_DIR}/Thirdparty/DBoW2/lib/libDBoW2.so
${PROJECT_SOURCE_DIR}/Thirdparty/g2o/lib/libg2o.so
)
# Build examples
# set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/Examples/RGB-D)
# add_executable(rgbd_tum
# Examples/RGB-D/rgbd_tum.cc)
# target_link_libraries(rgbd_tum ${PROJECT_NAME})
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/Examples/Stereo)
add_executable(stereo_kitti
Examples/Stereo/stereo_kitti.cc)
target_link_libraries(stereo_kitti ${PROJECT_NAME})
add_executable(stereo_kitti_raw
Examples/Stereo/stereo_kitti_raw.cc)
target_link_libraries(stereo_kitti_raw ${PROJECT_NAME})
# add_executable(stereo_euroc
# Examples/Stereo/stereo_euroc.cc)
# target_link_libraries(stereo_euroc ${PROJECT_NAME})
# set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/Examples/Monocular)
# add_executable(mono_tum
# Examples/Monocular/mono_tum.cc)
# target_link_libraries(mono_tum ${PROJECT_NAME})
# add_executable(mono_kitti
# Examples/Monocular/mono_kitti.cc)
# target_link_libraries(mono_kitti ${PROJECT_NAME})
# add_executable(mono_euroc
# Examples/Monocular/mono_euroc.cc)
# target_link_libraries(mono_euroc ${PROJECT_NAME})

837
CameraTrajectory.txt Normal file
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@ -0,0 +1,837 @@
1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000
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0.999999821 0.000600793 -0.000140831 -0.005287565 -0.000600734 0.999999821 0.000423221 -0.009078673 0.000141085 -0.000423136 0.999999881 0.665003479
0.999999881 0.000453820 -0.000058865 -0.007652601 -0.000453806 0.999999881 0.000234707 -0.011066814 0.000058972 -0.000234680 0.999999940 1.008891225
0.999999821 0.000538472 0.000024085 -0.012781111 -0.000538473 0.999999881 0.000037722 -0.014002660 -0.000024065 -0.000037734 1.000000000 1.352355361
0.999999881 0.000178701 0.000385942 -0.012698635 -0.000178820 0.999999940 0.000308846 -0.021051867 -0.000385887 -0.000308915 0.999999881 1.666168332
0.999999940 -0.000033520 0.000453690 -0.016401449 0.000033597 1.000000000 -0.000167819 -0.024129983 -0.000453685 0.000167834 0.999999940 1.996656537
1.000000000 0.000304019 0.000412484 -0.015632974 -0.000304002 0.999999940 -0.000041860 -0.035140179 -0.000412496 0.000041734 0.999999881 2.307993412
0.999999881 0.000526656 -0.000034597 -0.019121777 -0.000526651 0.999999821 0.000144192 -0.040023364 0.000034673 -0.000144174 1.000000000 2.637287378
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0.999999046 0.001100004 -0.000858403 -0.029538296 -0.001100121 0.999999404 -0.000136009 -0.040642641 0.000858253 0.000136953 0.999999642 3.278609514
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0.999998868 0.000690611 -0.001332756 -0.027410075 -0.000689793 0.999999583 0.000614410 -0.059132122 0.001333179 -0.000613490 0.999998868 3.874480724
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0.987189054 -0.009597487 -0.159266278 -1.968902588 0.011967507 0.999831378 0.013928384 0.410994530 0.159105778 -0.015655965 0.987137377 652.301696777
0.987172008 -0.009597956 -0.159372017 -1.967338562 0.011963438 0.999831915 0.013889679 0.409892082 0.159211934 -0.015618138 0.987120807 652.300292969
0.987168550 -0.009797839 -0.159380764 -1.967849731 0.012170329 0.999829054 0.013916370 0.409878731 0.159217179 -0.015677519 0.987119079 652.301696777
0.987171233 -0.009424097 -0.159386665 -1.968605042 0.011779406 0.999834776 0.013838987 0.410037041 0.159229919 -0.015538928 0.987119257 652.302673340
0.987162352 -0.009854227 -0.159416437 -1.967109680 0.012209930 0.999830186 0.013804274 0.410907745 0.159253314 -0.015573519 0.987114906 652.302612305
0.987167776 -0.009664430 -0.159393996 -1.966445923 0.012029589 0.999831378 0.013880217 0.409780502 0.159232989 -0.015619548 0.987117410 652.300292969
0.987162232 -0.009940899 -0.159411550 -1.967826843 0.012320157 0.999826789 0.013943873 0.410826683 0.159245312 -0.015728839 0.987113714 652.303344727
0.987164438 -0.009771828 -0.159408048 -1.967086792 0.012145424 0.999829292 0.013922559 0.411551476 0.159244806 -0.015679929 0.987114608 652.301818848
0.987179101 -0.009934327 -0.159307420 -1.968101501 0.012290964 0.999829054 0.013814500 0.412720680 0.159142971 -0.015595427 0.987132311 652.301940918
0.987156451 -0.009985086 -0.159444153 -1.964599609 0.012347409 0.999828160 0.013832148 0.414567947 0.159278646 -0.015623214 0.987110019 652.300170898
0.987168789 -0.009968393 -0.159368634 -1.967094421 0.012328748 0.999828398 0.013828766 0.415291786 0.159203455 -0.015616139 0.987122297 652.303283691

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 458.654
Camera.fy: 457.296
Camera.cx: 367.215
Camera.cy: 248.375
Camera.k1: -0.28340811
Camera.k2: 0.07395907
Camera.p1: 0.00019359
Camera.p2: 1.76187114e-05
# Camera frames per second
Camera.fps: 20.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#---------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 718.856
Camera.fy: 718.856
Camera.cx: 607.1928
Camera.cy: 185.2157
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
# Camera frames per second
Camera.fps: 10.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.1
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.15
Viewer.CameraLineWidth: 2
Viewer.ViewpointX: 0
Viewer.ViewpointY: -10
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 721.5377
Camera.fy: 721.5377
Camera.cx: 609.5593
Camera.cy: 172.854
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
# Camera frames per second
Camera.fps: 10.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.1
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.15
Viewer.CameraLineWidth: 2
Viewer.ViewpointX: 0
Viewer.ViewpointY: -10
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 707.0912
Camera.fy: 707.0912
Camera.cx: 601.8873
Camera.cy: 183.1104
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
# Camera frames per second
Camera.fps: 10.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.1
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.15
Viewer.CameraLineWidth: 2
Viewer.ViewpointX: 0
Viewer.ViewpointY: -10
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 517.306408
Camera.fy: 516.469215
Camera.cx: 318.643040
Camera.cy: 255.313989
Camera.k1: 0.262383
Camera.k2: -0.953104
Camera.p1: -0.005358
Camera.p2: 0.002628
Camera.k3: 1.163314
# Camera frames per second
Camera.fps: 30.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 520.908620
Camera.fy: 521.007327
Camera.cx: 325.141442
Camera.cy: 249.701764
Camera.k1: 0.231222
Camera.k2: -0.784899
Camera.p1: -0.003257
Camera.p2: -0.000105
Camera.k3: 0.917205
# Camera frames per second
Camera.fps: 30.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 535.4
Camera.fy: 539.2
Camera.cx: 320.1
Camera.cy: 247.6
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
# Camera frames per second
Camera.fps: 30.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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Examples/Monocular/mono_euroc.cc Normal file
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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include <unistd.h>
#include<opencv2/core/core.hpp>
#include<System.h>
using namespace std;
void LoadImages(const string &strImagePath, const string &strPathTimes,
vector<string> &vstrImages, vector<double> &vTimeStamps);
int main(int argc, char **argv)
{
if(argc != 5)
{
cerr << endl << "Usage: ./mono_tum path_to_vocabulary path_to_settings path_to_image_folder path_to_times_file" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageFilenames;
vector<double> vTimestamps;
LoadImages(string(argv[3]), string(argv[4]), vstrImageFilenames, vTimestamps);
int nImages = vstrImageFilenames.size();
if(nImages<=0)
{
cerr << "ERROR: Failed to load images" << endl;
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl << endl;
// Main loop
cv::Mat im;
for(int ni=0; ni<nImages; ni++)
{
// Read image from file
im = cv::imread(vstrImageFilenames[ni],CV_LOAD_IMAGE_UNCHANGED);
double tframe = vTimestamps[ni];
if(im.empty())
{
cerr << endl << "Failed to load image at: "
<< vstrImageFilenames[ni] << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the image to the SLAM system
SLAM.TrackMonocular(im,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// Wait to load the next frame
double T=0;
if(ni<nImages-1)
T = vTimestamps[ni+1]-tframe;
else if(ni>0)
T = tframe-vTimestamps[ni-1];
if(ttrack<T)
usleep((T-ttrack)*1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(),vTimesTrack.end());
float totaltime = 0;
for(int ni=0; ni<nImages; ni++)
{
totaltime+=vTimesTrack[ni];
}
cout << "-------" << endl << endl;
cout << "median tracking time: " << vTimesTrack[nImages/2] << endl;
cout << "mean tracking time: " << totaltime/nImages << endl;
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
return 0;
}
void LoadImages(const string &strImagePath, const string &strPathTimes,
vector<string> &vstrImages, vector<double> &vTimeStamps)
{
ifstream fTimes;
fTimes.open(strPathTimes.c_str());
vTimeStamps.reserve(5000);
vstrImages.reserve(5000);
while(!fTimes.eof())
{
string s;
getline(fTimes,s);
if(!s.empty())
{
stringstream ss;
ss << s;
vstrImages.push_back(strImagePath + "/" + ss.str() + ".png");
double t;
ss >> t;
vTimeStamps.push_back(t/1e9);
}
}
}

157
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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<iomanip>
#include <unistd.h>
#include<opencv2/core/core.hpp>
#include"System.h"
using namespace std;
void LoadImages(const string &strSequence, vector<string> &vstrImageFilenames,
vector<double> &vTimestamps);
int main(int argc, char **argv)
{
if(argc != 4)
{
cerr << endl << "Usage: ./mono_kitti path_to_vocabulary path_to_settings path_to_sequence" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageFilenames;
vector<double> vTimestamps;
LoadImages(string(argv[3]), vstrImageFilenames, vTimestamps);
int nImages = vstrImageFilenames.size();
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl << endl;
// Main loop
cv::Mat im;
for(int ni=0; ni<nImages; ni++)
{
// Read image from file
im = cv::imread(vstrImageFilenames[ni],CV_LOAD_IMAGE_UNCHANGED);
double tframe = vTimestamps[ni];
if(im.empty())
{
cerr << endl << "Failed to load image at: " << vstrImageFilenames[ni] << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the image to the SLAM system
SLAM.TrackMonocular(im,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// Wait to load the next frame
double T=0;
if(ni<nImages-1)
T = vTimestamps[ni+1]-tframe;
else if(ni>0)
T = tframe-vTimestamps[ni-1];
if(ttrack<T)
usleep((T-ttrack)*1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(),vTimesTrack.end());
float totaltime = 0;
for(int ni=0; ni<nImages; ni++)
{
totaltime+=vTimesTrack[ni];
}
cout << "-------" << endl << endl;
cout << "median tracking time: " << vTimesTrack[nImages/2] << endl;
cout << "mean tracking time: " << totaltime/nImages << endl;
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
return 0;
}
void LoadImages(const string &strPathToSequence, vector<string> &vstrImageFilenames, vector<double> &vTimestamps)
{
ifstream fTimes;
string strPathTimeFile = strPathToSequence + "/times.txt";
fTimes.open(strPathTimeFile.c_str());
while(!fTimes.eof())
{
string s;
getline(fTimes,s);
if(!s.empty())
{
stringstream ss;
ss << s;
double t;
ss >> t;
vTimestamps.push_back(t);
}
}
string strPrefixLeft = strPathToSequence + "/image_0/";
const int nTimes = vTimestamps.size();
vstrImageFilenames.resize(nTimes);
for(int i=0; i<nTimes; i++)
{
stringstream ss;
ss << setfill('0') << setw(6) << i;
vstrImageFilenames[i] = strPrefixLeft + ss.str() + ".png";
}
}

155
Examples/Monocular/mono_tum.cc Normal file
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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include <unistd.h>
#include<opencv2/core/core.hpp>
#include<System.h>
using namespace std;
void LoadImages(const string &strFile, vector<string> &vstrImageFilenames,
vector<double> &vTimestamps);
int main(int argc, char **argv)
{
if(argc != 4)
{
cerr << endl << "Usage: ./mono_tum path_to_vocabulary path_to_settings path_to_sequence" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageFilenames;
vector<double> vTimestamps;
string strFile = string(argv[3])+"/rgb.txt";
LoadImages(strFile, vstrImageFilenames, vTimestamps);
int nImages = vstrImageFilenames.size();
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl << endl;
// Main loop
cv::Mat im;
for(int ni=0; ni<nImages; ni++)
{
// Read image from file
im = cv::imread(string(argv[3])+"/"+vstrImageFilenames[ni],CV_LOAD_IMAGE_UNCHANGED);
double tframe = vTimestamps[ni];
if(im.empty())
{
cerr << endl << "Failed to load image at: "
<< string(argv[3]) << "/" << vstrImageFilenames[ni] << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the image to the SLAM system
SLAM.TrackMonocular(im,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// Wait to load the next frame
double T=0;
if(ni<nImages-1)
T = vTimestamps[ni+1]-tframe;
else if(ni>0)
T = tframe-vTimestamps[ni-1];
if(ttrack<T)
usleep((T-ttrack)*1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(),vTimesTrack.end());
float totaltime = 0;
for(int ni=0; ni<nImages; ni++)
{
totaltime+=vTimesTrack[ni];
}
cout << "-------" << endl << endl;
cout << "median tracking time: " << vTimesTrack[nImages/2] << endl;
cout << "mean tracking time: " << totaltime/nImages << endl;
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
return 0;
}
void LoadImages(const string &strFile, vector<string> &vstrImageFilenames, vector<double> &vTimestamps)
{
ifstream f;
f.open(strFile.c_str());
// skip first three lines
string s0;
getline(f,s0);
getline(f,s0);
getline(f,s0);
while(!f.eof())
{
string s;
getline(f,s);
if(!s.empty())
{
stringstream ss;
ss << s;
double t;
string sRGB;
ss >> t;
vTimestamps.push_back(t);
ss >> sRGB;
vstrImageFilenames.push_back(sRGB);
}
}
}

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 517.306408
Camera.fy: 516.469215
Camera.cx: 318.643040
Camera.cy: 255.313989
Camera.k1: 0.262383
Camera.k2: -0.953104
Camera.p1: -0.005358
Camera.p2: 0.002628
Camera.k3: 1.163314
Camera.width: 640
Camera.height: 480
# Camera frames per second
Camera.fps: 30.0
# IR projector baseline times fx (aprox.)
Camera.bf: 40.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40.0
# Deptmap values factor
DepthMapFactor: 5000.0
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

70
Examples/RGB-D/TUM2.yaml Normal file
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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 520.908620
Camera.fy: 521.007327
Camera.cx: 325.141442
Camera.cy: 249.701764
Camera.k1: 0.231222
Camera.k2: -0.784899
Camera.p1: -0.003257
Camera.p2: -0.000105
Camera.k3: 0.917205
Camera.width: 640
Camera.height: 480
# Camera frames per second
Camera.fps: 30.0
# IR projector baseline times fx (aprox.)
Camera.bf: 40.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40.0
# Deptmap values factor
DepthMapFactor: 5208.0
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

69
Examples/RGB-D/TUM3.yaml Normal file
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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 535.4
Camera.fy: 539.2
Camera.cx: 320.1
Camera.cy: 247.6
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 640
Camera.height: 480
# Camera frames per second
Camera.fps: 30.0
# IR projector baseline times fx (aprox.)
Camera.bf: 40.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40.0
# Deptmap values factor
DepthMapFactor: 5000.0
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

573
Examples/RGB-D/associations/fr1_desk.txt Normal file
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Examples/RGB-D/associations/fr3_str_tex_near.txt Normal file
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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include <iostream>
#include <algorithm>
#include <fstream>
#include <chrono>
#include <unistd.h>
#include <opencv2/core/core.hpp>
#include <System.h>
using namespace std;
void LoadImages(const string &strAssociationFilename, vector<string> &vstrImageFilenamesRGB,
vector<string> &vstrImageFilenamesD, vector<double> &vTimestamps);
void LoadLabel(const string &strLabelFilename, vector<vector<double>> &vLabel);
int main(int argc, char **argv)
{
if (argc != 5)
{
cerr << endl
<< "Usage: ./rgbd_tum path_to_vocabulary path_to_settings path_to_sequence path_to_association" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageFilenamesRGB;
vector<string> vstrImageFilenamesD;
vector<double> vTimestamps;
string strAssociationFilename = string(argv[4]);
LoadImages(strAssociationFilename, vstrImageFilenamesRGB, vstrImageFilenamesD, vTimestamps);
// Check consistency in the number of images and depthmaps
int nImages = vstrImageFilenamesRGB.size();
if (vstrImageFilenamesRGB.empty())
{
cerr << endl
<< "No images found in provided path." << endl;
return 1;
}
else if (vstrImageFilenamesD.size() != vstrImageFilenamesRGB.size())
{
cerr << endl
<< "Different number of images for rgb and depth." << endl;
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1], argv[2], ORB_SLAM2::System::RGBD, true); // argv[1]: rgbd_tum argv[2]:ORB_voc.txt
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl
<< "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl
<< endl;
// Main loop
cv::Mat imRGB, imD;
for (int ni = 0; ni < nImages; ni++)
{
vector<vector<double>> vLabel;
// Read image and depthmap from file
imRGB = cv::imread(string(argv[3]) + "/" + vstrImageFilenamesRGB[ni], CV_LOAD_IMAGE_UNCHANGED); // argv[3]:data/dynamic_objects/rgbd_dataset_freiburg3_walking_static
imD = cv::imread(string(argv[3]) + "/" + vstrImageFilenamesD[ni], CV_LOAD_IMAGE_UNCHANGED);
LoadLabel(string(argv[3]) + "/labels/" + to_string(vTimestamps[ni]) + ".txt", vLabel);
// cout << "i " << ni << endl;
// cout << "size " << vLabel.size() << endl;
// cout << "filename " << setiosflags(ios::fixed)<<setprecision(6) << vTimestamps[ni] << endl;
// for(int i = 0; i < vLabel.size(); i++)
// {
// cout << vLabel[i][0] << " " ;
// cout << vLabel[i][1] << " " ;
// cout << vLabel[i][2] << " " ;
// cout << vLabel[i][3] << " " ;
// cout << vLabel[i][4] << " " ;
// cout << endl;
// }
double tframe = vTimestamps[ni];
if (imRGB.empty())
{
cerr << endl
<< "Failed to load image at: "
<< string(argv[3]) << "/" << vstrImageFilenamesRGB[ni] << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the image to the SLAM system
// for(int i =0;i<imD.rows;i++)
// {
// for(int j=0;j<imD.cols;j++)
// {
// if(imD.at<float>(i,j)<0)
// {
// imD.at<float>(i,j) = 0;
// }
// }
// }
SLAM.TrackRGBD(imRGB, imD, tframe, vLabel);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack = std::chrono::duration_cast<std::chrono::duration<double>>(t2 - t1).count();
vTimesTrack[ni] = ttrack;
// Wait to load the next frame
double T = 0;
if (ni < nImages - 1)
T = vTimestamps[ni + 1] - tframe;
else if (ni > 0)
T = tframe - vTimestamps[ni - 1];
if (ttrack < T)
usleep((T - ttrack) * 1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(), vTimesTrack.end());
float totaltime = 0;
for (int ni = 0; ni < nImages; ni++)
{
totaltime += vTimesTrack[ni];
}
cout << "-------" << endl
<< endl;
cout << "median tracking time: " << vTimesTrack[nImages / 2] << endl;
cout << "mean tracking time: " << totaltime / nImages << endl;
// Save camera trajectory
SLAM.SaveTrajectoryTUM("CameraTrajectory.txt");
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
return 0;
}
void LoadImages(const string &strAssociationFilename, vector<string> &vstrImageFilenamesRGB,
vector<string> &vstrImageFilenamesD, vector<double> &vTimestamps)
{
ifstream fAssociation;
fAssociation.open(strAssociationFilename.c_str());
while (!fAssociation.eof())
{
string s;
getline(fAssociation, s);
if (!s.empty())
{
stringstream ss;
ss << s;
double t;
string sRGB, sD;
ss >> t;
vTimestamps.push_back(t);
ss >> sRGB;
vstrImageFilenamesRGB.push_back(sRGB);
ss >> t;
ss >> sD;
vstrImageFilenamesD.push_back(sD);
}
}
}
void LoadLabel(const string &strLabelFilename, vector<vector<double>> &vLabel)
{
ifstream fAssociation;
fAssociation.open(strLabelFilename.c_str());
while (!fAssociation.eof())
{
string s;
getline(fAssociation, s);
if (!s.empty())
{
stringstream ss;
ss << s;
double cls, x, y, w, h;
vector<double> label;
ss >> cls;
label.push_back(cls);
ss >> x;
label.push_back(x);
ss >> y;
label.push_back(y);
ss >> w;
label.push_back(w);
ss >> h;
label.push_back(h);
vLabel.push_back(label);
// if (cls == 0)
// {
// label.push_back(cls);
// ss >> x;
// label.push_back(x);
// ss >> y;
// label.push_back(y);
// ss >> w;
// label.push_back(w);
// ss >> h;
// label.push_back(h);
// vLabel.push_back(label);
// }
// else
// {
// ss >> x;
// ss >> y;
// ss >> w;
// ss >> h;
// }
}
}
}

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Examples/ROS/ORB_SLAM2/Asus.yaml Normal file
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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 535.4
Camera.fy: 539.2
Camera.cx: 320.1
Camera.cy: 247.6
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 640
Camera.height: 480
# Camera frames per second
Camera.fps: 30.0
# IR projector baseline times fx (aprox.)
Camera.bf: 40.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40.0
# Deptmap values factor
DepthMapFactor: 1.0
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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cmake_minimum_required(VERSION 2.4.6)
include($ENV{ROS_ROOT}/core/rosbuild/rosbuild.cmake)
add_definitions(-w)
rosbuild_init()
IF(NOT ROS_BUILD_TYPE)
SET(ROS_BUILD_TYPE Release)
ENDIF()
MESSAGE("Build type: " ${ROS_BUILD_TYPE})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -O3 -march=native ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -march=native")
# Check C++11 or C++0x support
include(CheckCXXCompilerFlag)
CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
CHECK_CXX_COMPILER_FLAG("-std=c++0x" COMPILER_SUPPORTS_CXX0X)
if(COMPILER_SUPPORTS_CXX11)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
add_definitions(-DCOMPILEDWITHC11)
message(STATUS "Using flag -std=c++11.")
elseif(COMPILER_SUPPORTS_CXX0X)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x")
add_definitions(-DCOMPILEDWITHC0X)
message(STATUS "Using flag -std=c++0x.")
else()
message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} has no C++11 support. Please use a different C++ compiler.")
endif()
LIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/../../../cmake_modules)
find_package(OpenCV 3.0 QUIET)
if(NOT OpenCV_FOUND)
find_package(OpenCV 2.4.3 QUIET)
if(NOT OpenCV_FOUND)
message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
endif()
endif()
find_package(Eigen3 3.1.0 REQUIRED)
find_package(Pangolin REQUIRED)
include_directories(
${PROJECT_SOURCE_DIR}
${PROJECT_SOURCE_DIR}/../../../
${PROJECT_SOURCE_DIR}/../../../include
${Pangolin_INCLUDE_DIRS}
)
set(LIBS
${OpenCV_LIBS}
${EIGEN3_LIBS}
${Pangolin_LIBRARIES}
${PROJECT_SOURCE_DIR}/../../../Thirdparty/DBoW2/lib/libDBoW2.so
${PROJECT_SOURCE_DIR}/../../../Thirdparty/g2o/lib/libg2o.so
${PROJECT_SOURCE_DIR}/../../../lib/libORB_SLAM2.so
${PROJECT_SOURCE_DIR}/../../../lib/libboost_filesystem.so
${PROJECT_SOURCE_DIR}/../../../lib/libboost_system.so
-lboost_system
)
# Node for monocular camera
rosbuild_add_executable(Mono
src/ros_mono.cc
)
target_link_libraries(Mono
${LIBS}
)
# Node for monocular camera (Augmented Reality Demo)
rosbuild_add_executable(MonoAR
src/AR/ros_mono_ar.cc
src/AR/ViewerAR.h
src/AR/ViewerAR.cc
)
target_link_libraries(MonoAR
${LIBS}
)
# Node for stereo camera
rosbuild_add_executable(Stereo
src/ros_stereo.cc
)
target_link_libraries(Stereo
${LIBS}
)
# Node for RGB-D camera
rosbuild_add_executable(RGBD
src/ros_rgbd.cc
)
target_link_libraries(RGBD
${LIBS}
)

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<package>
<description brief="ORB_SLAM2">
ORB_SLAM2
</description>
<author>Raul Mur-Artal</author>
<license>GPLv3</license>
<depend package="roscpp"/>
<depend package="tf"/>
<depend package="sensor_msgs"/>
<depend package="image_transport"/>
<depend package="cv_bridge"/>
</package>

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include "ViewerAR.h"
#include <opencv2/highgui/highgui.hpp>
#include <unistd.h>
#include <mutex>
#include <thread>
#include <cstdlib>
using namespace std;
namespace ORB_SLAM2
{
const float eps = 1e-4;
cv::Mat ExpSO3(const float &x, const float &y, const float &z)
{
cv::Mat I = cv::Mat::eye(3,3,CV_32F);
const float d2 = x*x+y*y+z*z;
const float d = sqrt(d2);
cv::Mat W = (cv::Mat_<float>(3,3) << 0, -z, y,
z, 0, -x,
-y, x, 0);
if(d<eps)
return (I + W + 0.5f*W*W);
else
return (I + W*sin(d)/d + W*W*(1.0f-cos(d))/d2);
}
cv::Mat ExpSO3(const cv::Mat &v)
{
return ExpSO3(v.at<float>(0),v.at<float>(1),v.at<float>(2));
}
ViewerAR::ViewerAR(){}
void ViewerAR::Run()
{
int w,h,wui;
cv::Mat im, Tcw;
int status;
vector<cv::KeyPoint> vKeys;
vector<MapPoint*> vMPs;
while(1)
{
GetImagePose(im,Tcw,status,vKeys,vMPs);
if(im.empty())
cv::waitKey(mT);
else
{
w = im.cols;
h = im.rows;
break;
}
}
wui=200;
pangolin::CreateWindowAndBind("Viewer",w+wui,h);
glEnable(GL_DEPTH_TEST);
glEnable (GL_BLEND);
pangolin::CreatePanel("menu").SetBounds(0.0,1.0,0.0,pangolin::Attach::Pix(wui));
pangolin::Var<bool> menu_detectplane("menu.Insert Cube",false,false);
pangolin::Var<bool> menu_clear("menu.Clear All",false,false);
pangolin::Var<bool> menu_drawim("menu.Draw Image",true,true);
pangolin::Var<bool> menu_drawcube("menu.Draw Cube",true,true);
pangolin::Var<float> menu_cubesize("menu. Cube Size",0.05,0.01,0.3);
pangolin::Var<bool> menu_drawgrid("menu.Draw Grid",true,true);
pangolin::Var<int> menu_ngrid("menu. Grid Elements",3,1,10);
pangolin::Var<float> menu_sizegrid("menu. Element Size",0.05,0.01,0.3);
pangolin::Var<bool> menu_drawpoints("menu.Draw Points",false,true);
pangolin::Var<bool> menu_LocalizationMode("menu.Localization Mode",false,true);
bool bLocalizationMode = false;
pangolin::View& d_image = pangolin::Display("image")
.SetBounds(0,1.0f,pangolin::Attach::Pix(wui),1.0f,(float)w/h)
.SetLock(pangolin::LockLeft, pangolin::LockTop);
pangolin::GlTexture imageTexture(w,h,GL_RGB,false,0,GL_RGB,GL_UNSIGNED_BYTE);
pangolin::OpenGlMatrixSpec P = pangolin::ProjectionMatrixRDF_TopLeft(w,h,fx,fy,cx,cy,0.001,1000);
vector<Plane*> vpPlane;
while(1)
{
if(menu_LocalizationMode && !bLocalizationMode)
{
mpSystem->ActivateLocalizationMode();
bLocalizationMode = true;
}
else if(!menu_LocalizationMode && bLocalizationMode)
{
mpSystem->DeactivateLocalizationMode();
bLocalizationMode = false;
}
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
// Activate camera view
d_image.Activate();
glColor3f(1.0,1.0,1.0);
// Get last image and its computed pose from SLAM
GetImagePose(im,Tcw,status,vKeys,vMPs);
// Add text to image
PrintStatus(status,bLocalizationMode,im);
if(menu_drawpoints)
DrawTrackedPoints(vKeys,vMPs,im);
// Draw image
if(menu_drawim)
DrawImageTexture(imageTexture,im);
glClear(GL_DEPTH_BUFFER_BIT);
// Load camera projection
glMatrixMode(GL_PROJECTION);
P.Load();
glMatrixMode(GL_MODELVIEW);
// Load camera pose
LoadCameraPose(Tcw);
// Draw virtual things
if(status==2)
{
if(menu_clear)
{
if(!vpPlane.empty())
{
for(size_t i=0; i<vpPlane.size(); i++)
{
delete vpPlane[i];
}
vpPlane.clear();
cout << "All cubes erased!" << endl;
}
menu_clear = false;
}
if(menu_detectplane)
{
Plane* pPlane = DetectPlane(Tcw,vMPs,50);
if(pPlane)
{
cout << "New virtual cube inserted!" << endl;
vpPlane.push_back(pPlane);
}
else
{
cout << "No plane detected. Point the camera to a planar region." << endl;
}
menu_detectplane = false;
}
if(!vpPlane.empty())
{
// Recompute plane if there has been a loop closure or global BA
// In localization mode, map is not updated so we do not need to recompute
bool bRecompute = false;
if(!bLocalizationMode)
{
if(mpSystem->MapChanged())
{
cout << "Map changed. All virtual elements are recomputed!" << endl;
bRecompute = true;
}
}
for(size_t i=0; i<vpPlane.size(); i++)
{
Plane* pPlane = vpPlane[i];
if(pPlane)
{
if(bRecompute)
{
pPlane->Recompute();
}
glPushMatrix();
pPlane->glTpw.Multiply();
// Draw cube
if(menu_drawcube)
{
DrawCube(menu_cubesize);
}
// Draw grid plane
if(menu_drawgrid)
{
DrawPlane(menu_ngrid,menu_sizegrid);
}
glPopMatrix();
}
}
}
}
pangolin::FinishFrame();
usleep(mT*1000);
}
}
void ViewerAR::SetImagePose(const cv::Mat &im, const cv::Mat &Tcw, const int &status, const vector<cv::KeyPoint> &vKeys, const vector<ORB_SLAM2::MapPoint*> &vMPs)
{
unique_lock<mutex> lock(mMutexPoseImage);
mImage = im.clone();
mTcw = Tcw.clone();
mStatus = status;
mvKeys = vKeys;
mvMPs = vMPs;
}
void ViewerAR::GetImagePose(cv::Mat &im, cv::Mat &Tcw, int &status, std::vector<cv::KeyPoint> &vKeys, std::vector<MapPoint*> &vMPs)
{
unique_lock<mutex> lock(mMutexPoseImage);
im = mImage.clone();
Tcw = mTcw.clone();
status = mStatus;
vKeys = mvKeys;
vMPs = mvMPs;
}
void ViewerAR::LoadCameraPose(const cv::Mat &Tcw)
{
if(!Tcw.empty())
{
pangolin::OpenGlMatrix M;
M.m[0] = Tcw.at<float>(0,0);
M.m[1] = Tcw.at<float>(1,0);
M.m[2] = Tcw.at<float>(2,0);
M.m[3] = 0.0;
M.m[4] = Tcw.at<float>(0,1);
M.m[5] = Tcw.at<float>(1,1);
M.m[6] = Tcw.at<float>(2,1);
M.m[7] = 0.0;
M.m[8] = Tcw.at<float>(0,2);
M.m[9] = Tcw.at<float>(1,2);
M.m[10] = Tcw.at<float>(2,2);
M.m[11] = 0.0;
M.m[12] = Tcw.at<float>(0,3);
M.m[13] = Tcw.at<float>(1,3);
M.m[14] = Tcw.at<float>(2,3);
M.m[15] = 1.0;
M.Load();
}
}
void ViewerAR::PrintStatus(const int &status, const bool &bLocMode, cv::Mat &im)
{
if(!bLocMode)
{
switch(status)
{
case 1: {AddTextToImage("SLAM NOT INITIALIZED",im,255,0,0); break;}
case 2: {AddTextToImage("SLAM ON",im,0,255,0); break;}
case 3: {AddTextToImage("SLAM LOST",im,255,0,0); break;}
}
}
else
{
switch(status)
{
case 1: {AddTextToImage("SLAM NOT INITIALIZED",im,255,0,0); break;}
case 2: {AddTextToImage("LOCALIZATION ON",im,0,255,0); break;}
case 3: {AddTextToImage("LOCALIZATION LOST",im,255,0,0); break;}
}
}
}
void ViewerAR::AddTextToImage(const string &s, cv::Mat &im, const int r, const int g, const int b)
{
int l = 10;
//imText.rowRange(im.rows-imText.rows,imText.rows) = cv::Mat::zeros(textSize.height+10,im.cols,im.type());
cv::putText(im,s,cv::Point(l,im.rows-l),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l-1,im.rows-l),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l+1,im.rows-l),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l-1,im.rows-(l-1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l,im.rows-(l-1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l+1,im.rows-(l-1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l-1,im.rows-(l+1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l,im.rows-(l+1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l+1,im.rows-(l+1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l,im.rows-l),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(r,g,b),2,8);
}
void ViewerAR::DrawImageTexture(pangolin::GlTexture &imageTexture, cv::Mat &im)
{
if(!im.empty())
{
imageTexture.Upload(im.data,GL_RGB,GL_UNSIGNED_BYTE);
imageTexture.RenderToViewportFlipY();
}
}
void ViewerAR::DrawCube(const float &size,const float x, const float y, const float z)
{
pangolin::OpenGlMatrix M = pangolin::OpenGlMatrix::Translate(-x,-size-y,-z);
glPushMatrix();
M.Multiply();
pangolin::glDrawColouredCube(-size,size);
glPopMatrix();
}
void ViewerAR::DrawPlane(Plane *pPlane, int ndivs, float ndivsize)
{
glPushMatrix();
pPlane->glTpw.Multiply();
DrawPlane(ndivs,ndivsize);
glPopMatrix();
}
void ViewerAR::DrawPlane(int ndivs, float ndivsize)
{
// Plane parallel to x-z at origin with normal -y
const float minx = -ndivs*ndivsize;
const float minz = -ndivs*ndivsize;
const float maxx = ndivs*ndivsize;
const float maxz = ndivs*ndivsize;
glLineWidth(2);
glColor3f(0.7f,0.7f,1.0f);
glBegin(GL_LINES);
for(int n = 0; n<=2*ndivs; n++)
{
glVertex3f(minx+ndivsize*n,0,minz);
glVertex3f(minx+ndivsize*n,0,maxz);
glVertex3f(minx,0,minz+ndivsize*n);
glVertex3f(maxx,0,minz+ndivsize*n);
}
glEnd();
}
void ViewerAR::DrawTrackedPoints(const std::vector<cv::KeyPoint> &vKeys, const std::vector<MapPoint *> &vMPs, cv::Mat &im)
{
const int N = vKeys.size();
for(int i=0; i<N; i++)
{
if(vMPs[i])
{
cv::circle(im,vKeys[i].pt,1,cv::Scalar(0,255,0),-1);
}
}
}
Plane* ViewerAR::DetectPlane(const cv::Mat Tcw, const std::vector<MapPoint*> &vMPs, const int iterations)
{
// Retrieve 3D points
vector<cv::Mat> vPoints;
vPoints.reserve(vMPs.size());
vector<MapPoint*> vPointMP;
vPointMP.reserve(vMPs.size());
for(size_t i=0; i<vMPs.size(); i++)
{
MapPoint* pMP=vMPs[i];
if(pMP)
{
if(pMP->Observations()>5)
{
vPoints.push_back(pMP->GetWorldPos());
vPointMP.push_back(pMP);
}
}
}
const int N = vPoints.size();
if(N<50)
return NULL;
// Indices for minimum set selection
vector<size_t> vAllIndices;
vAllIndices.reserve(N);
vector<size_t> vAvailableIndices;
for(int i=0; i<N; i++)
{
vAllIndices.push_back(i);
}
float bestDist = 1e10;
vector<float> bestvDist;
//RANSAC
for(int n=0; n<iterations; n++)
{
vAvailableIndices = vAllIndices;
cv::Mat A(3,4,CV_32F);
A.col(3) = cv::Mat::ones(3,1,CV_32F);
// Get min set of points
for(short i = 0; i < 3; ++i)
{
int randi = DUtils::Random::RandomInt(0, vAvailableIndices.size()-1);
int idx = vAvailableIndices[randi];
A.row(i).colRange(0,3) = vPoints[idx].t();
vAvailableIndices[randi] = vAvailableIndices.back();
vAvailableIndices.pop_back();
}
cv::Mat u,w,vt;
cv::SVDecomp(A,w,u,vt,cv::SVD::MODIFY_A | cv::SVD::FULL_UV);
const float a = vt.at<float>(3,0);
const float b = vt.at<float>(3,1);
const float c = vt.at<float>(3,2);
const float d = vt.at<float>(3,3);
vector<float> vDistances(N,0);
const float f = 1.0f/sqrt(a*a+b*b+c*c+d*d);
for(int i=0; i<N; i++)
{
vDistances[i] = fabs(vPoints[i].at<float>(0)*a+vPoints[i].at<float>(1)*b+vPoints[i].at<float>(2)*c+d)*f;
}
vector<float> vSorted = vDistances;
sort(vSorted.begin(),vSorted.end());
int nth = max((int)(0.2*N),20);
const float medianDist = vSorted[nth];
if(medianDist<bestDist)
{
bestDist = medianDist;
bestvDist = vDistances;
}
}
// Compute threshold inlier/outlier
const float th = 1.4*bestDist;
vector<bool> vbInliers(N,false);
int nInliers = 0;
for(int i=0; i<N; i++)
{
if(bestvDist[i]<th)
{
nInliers++;
vbInliers[i]=true;
}
}
vector<MapPoint*> vInlierMPs(nInliers,NULL);
int nin = 0;
for(int i=0; i<N; i++)
{
if(vbInliers[i])
{
vInlierMPs[nin] = vPointMP[i];
nin++;
}
}
return new Plane(vInlierMPs,Tcw);
}
Plane::Plane(const std::vector<MapPoint *> &vMPs, const cv::Mat &Tcw):mvMPs(vMPs),mTcw(Tcw.clone())
{
rang = -3.14f/2+((float)rand()/RAND_MAX)*3.14f;
Recompute();
}
void Plane::Recompute()
{
const int N = mvMPs.size();
// Recompute plane with all points
cv::Mat A = cv::Mat(N,4,CV_32F);
A.col(3) = cv::Mat::ones(N,1,CV_32F);
o = cv::Mat::zeros(3,1,CV_32F);
int nPoints = 0;
for(int i=0; i<N; i++)
{
MapPoint* pMP = mvMPs[i];
if(!pMP->isBad())
{
cv::Mat Xw = pMP->GetWorldPos();
o+=Xw;
A.row(nPoints).colRange(0,3) = Xw.t();
nPoints++;
}
}
A.resize(nPoints);
cv::Mat u,w,vt;
cv::SVDecomp(A,w,u,vt,cv::SVD::MODIFY_A | cv::SVD::FULL_UV);
float a = vt.at<float>(3,0);
float b = vt.at<float>(3,1);
float c = vt.at<float>(3,2);
o = o*(1.0f/nPoints);
const float f = 1.0f/sqrt(a*a+b*b+c*c);
// Compute XC just the first time
if(XC.empty())
{
cv::Mat Oc = -mTcw.colRange(0,3).rowRange(0,3).t()*mTcw.rowRange(0,3).col(3);
XC = Oc-o;
}
if((XC.at<float>(0)*a+XC.at<float>(1)*b+XC.at<float>(2)*c)>0)
{
a=-a;
b=-b;
c=-c;
}
const float nx = a*f;
const float ny = b*f;
const float nz = c*f;
n = (cv::Mat_<float>(3,1)<<nx,ny,nz);
cv::Mat up = (cv::Mat_<float>(3,1) << 0.0f, 1.0f, 0.0f);
cv::Mat v = up.cross(n);
const float sa = cv::norm(v);
const float ca = up.dot(n);
const float ang = atan2(sa,ca);
Tpw = cv::Mat::eye(4,4,CV_32F);
Tpw.rowRange(0,3).colRange(0,3) = ExpSO3(v*ang/sa)*ExpSO3(up*rang);
o.copyTo(Tpw.col(3).rowRange(0,3));
glTpw.m[0] = Tpw.at<float>(0,0);
glTpw.m[1] = Tpw.at<float>(1,0);
glTpw.m[2] = Tpw.at<float>(2,0);
glTpw.m[3] = 0.0;
glTpw.m[4] = Tpw.at<float>(0,1);
glTpw.m[5] = Tpw.at<float>(1,1);
glTpw.m[6] = Tpw.at<float>(2,1);
glTpw.m[7] = 0.0;
glTpw.m[8] = Tpw.at<float>(0,2);
glTpw.m[9] = Tpw.at<float>(1,2);
glTpw.m[10] = Tpw.at<float>(2,2);
glTpw.m[11] = 0.0;
glTpw.m[12] = Tpw.at<float>(0,3);
glTpw.m[13] = Tpw.at<float>(1,3);
glTpw.m[14] = Tpw.at<float>(2,3);
glTpw.m[15] = 1.0;
}
Plane::Plane(const float &nx, const float &ny, const float &nz, const float &ox, const float &oy, const float &oz)
{
n = (cv::Mat_<float>(3,1)<<nx,ny,nz);
o = (cv::Mat_<float>(3,1)<<ox,oy,oz);
cv::Mat up = (cv::Mat_<float>(3,1) << 0.0f, 1.0f, 0.0f);
cv::Mat v = up.cross(n);
const float s = cv::norm(v);
const float c = up.dot(n);
const float a = atan2(s,c);
Tpw = cv::Mat::eye(4,4,CV_32F);
const float rang = -3.14f/2+((float)rand()/RAND_MAX)*3.14f;
cout << rang;
Tpw.rowRange(0,3).colRange(0,3) = ExpSO3(v*a/s)*ExpSO3(up*rang);
o.copyTo(Tpw.col(3).rowRange(0,3));
glTpw.m[0] = Tpw.at<float>(0,0);
glTpw.m[1] = Tpw.at<float>(1,0);
glTpw.m[2] = Tpw.at<float>(2,0);
glTpw.m[3] = 0.0;
glTpw.m[4] = Tpw.at<float>(0,1);
glTpw.m[5] = Tpw.at<float>(1,1);
glTpw.m[6] = Tpw.at<float>(2,1);
glTpw.m[7] = 0.0;
glTpw.m[8] = Tpw.at<float>(0,2);
glTpw.m[9] = Tpw.at<float>(1,2);
glTpw.m[10] = Tpw.at<float>(2,2);
glTpw.m[11] = 0.0;
glTpw.m[12] = Tpw.at<float>(0,3);
glTpw.m[13] = Tpw.at<float>(1,3);
glTpw.m[14] = Tpw.at<float>(2,3);
glTpw.m[15] = 1.0;
}
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef VIEWERAR_H
#define VIEWERAR_H
#include <mutex>
#include <opencv2/core/core.hpp>
#include <pangolin/pangolin.h>
#include <string>
#include"../../../include/System.h"
namespace ORB_SLAM2
{
class Plane
{
public:
Plane(const std::vector<MapPoint*> &vMPs, const cv::Mat &Tcw);
Plane(const float &nx, const float &ny, const float &nz, const float &ox, const float &oy, const float &oz);
void Recompute();
//normal
cv::Mat n;
//origin
cv::Mat o;
//arbitrary orientation along normal
float rang;
//transformation from world to the plane
cv::Mat Tpw;
pangolin::OpenGlMatrix glTpw;
//MapPoints that define the plane
std::vector<MapPoint*> mvMPs;
//camera pose when the plane was first observed (to compute normal direction)
cv::Mat mTcw, XC;
};
class ViewerAR
{
public:
ViewerAR();
void SetFPS(const float fps){
mFPS = fps;
mT=1e3/fps;
}
void SetSLAM(ORB_SLAM2::System* pSystem){
mpSystem = pSystem;
}
// Main thread function.
void Run();
void SetCameraCalibration(const float &fx_, const float &fy_, const float &cx_, const float &cy_){
fx = fx_; fy = fy_; cx = cx_; cy = cy_;
}
void SetImagePose(const cv::Mat &im, const cv::Mat &Tcw, const int &status,
const std::vector<cv::KeyPoint> &vKeys, const std::vector<MapPoint*> &vMPs);
void GetImagePose(cv::Mat &im, cv::Mat &Tcw, int &status,
std::vector<cv::KeyPoint> &vKeys, std::vector<MapPoint*> &vMPs);
private:
//SLAM
ORB_SLAM2::System* mpSystem;
void PrintStatus(const int &status, const bool &bLocMode, cv::Mat &im);
void AddTextToImage(const std::string &s, cv::Mat &im, const int r=0, const int g=0, const int b=0);
void LoadCameraPose(const cv::Mat &Tcw);
void DrawImageTexture(pangolin::GlTexture &imageTexture, cv::Mat &im);
void DrawCube(const float &size, const float x=0, const float y=0, const float z=0);
void DrawPlane(int ndivs, float ndivsize);
void DrawPlane(Plane* pPlane, int ndivs, float ndivsize);
void DrawTrackedPoints(const std::vector<cv::KeyPoint> &vKeys, const std::vector<MapPoint*> &vMPs, cv::Mat &im);
Plane* DetectPlane(const cv::Mat Tcw, const std::vector<MapPoint*> &vMPs, const int iterations=50);
// frame rate
float mFPS, mT;
float fx,fy,cx,cy;
// Last processed image and computed pose by the SLAM
std::mutex mMutexPoseImage;
cv::Mat mTcw;
cv::Mat mImage;
int mStatus;
std::vector<cv::KeyPoint> mvKeys;
std::vector<MapPoint*> mvMPs;
};
}
#endif // VIEWERAR_H

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<ros/ros.h>
#include <cv_bridge/cv_bridge.h>
#include<opencv2/core/core.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include"../../../include/System.h"
#include"ViewerAR.h"
using namespace std;
ORB_SLAM2::ViewerAR viewerAR;
bool bRGB = true;
cv::Mat K;
cv::Mat DistCoef;
class ImageGrabber
{
public:
ImageGrabber(ORB_SLAM2::System* pSLAM):mpSLAM(pSLAM){}
void GrabImage(const sensor_msgs::ImageConstPtr& msg);
ORB_SLAM2::System* mpSLAM;
};
int main(int argc, char **argv)
{
ros::init(argc, argv, "Mono");
ros::start();
if(argc != 3)
{
cerr << endl << "Usage: rosrun ORB_SLAM2 Mono path_to_vocabulary path_to_settings" << endl;
ros::shutdown();
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,false);
cout << endl << endl;
cout << "-----------------------" << endl;
cout << "Augmented Reality Demo" << endl;
cout << "1) Translate the camera to initialize SLAM." << endl;
cout << "2) Look at a planar region and translate the camera." << endl;
cout << "3) Press Insert Cube to place a virtual cube in the plane. " << endl;
cout << endl;
cout << "You can place several cubes in different planes." << endl;
cout << "-----------------------" << endl;
cout << endl;
viewerAR.SetSLAM(&SLAM);
ImageGrabber igb(&SLAM);
ros::NodeHandle nodeHandler;
ros::Subscriber sub = nodeHandler.subscribe("/camera/image_raw", 1, &ImageGrabber::GrabImage,&igb);
cv::FileStorage fSettings(argv[2], cv::FileStorage::READ);
bRGB = static_cast<bool>((int)fSettings["Camera.RGB"]);
float fps = fSettings["Camera.fps"];
viewerAR.SetFPS(fps);
float fx = fSettings["Camera.fx"];
float fy = fSettings["Camera.fy"];
float cx = fSettings["Camera.cx"];
float cy = fSettings["Camera.cy"];
viewerAR.SetCameraCalibration(fx,fy,cx,cy);
K = cv::Mat::eye(3,3,CV_32F);
K.at<float>(0,0) = fx;
K.at<float>(1,1) = fy;
K.at<float>(0,2) = cx;
K.at<float>(1,2) = cy;
DistCoef = cv::Mat::zeros(4,1,CV_32F);
DistCoef.at<float>(0) = fSettings["Camera.k1"];
DistCoef.at<float>(1) = fSettings["Camera.k2"];
DistCoef.at<float>(2) = fSettings["Camera.p1"];
DistCoef.at<float>(3) = fSettings["Camera.p2"];
const float k3 = fSettings["Camera.k3"];
if(k3!=0)
{
DistCoef.resize(5);
DistCoef.at<float>(4) = k3;
}
thread tViewer = thread(&ORB_SLAM2::ViewerAR::Run,&viewerAR);
ros::spin();
// Stop all threads
SLAM.Shutdown();
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
ros::shutdown();
return 0;
}
void ImageGrabber::GrabImage(const sensor_msgs::ImageConstPtr& msg)
{
// Copy the ros image message to cv::Mat.
cv_bridge::CvImageConstPtr cv_ptr;
try
{
cv_ptr = cv_bridge::toCvShare(msg);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
cv::Mat im = cv_ptr->image.clone();
cv::Mat imu;
cv::Mat Tcw = mpSLAM->TrackMonocular(cv_ptr->image,cv_ptr->header.stamp.toSec());
int state = mpSLAM->GetTrackingState();
vector<ORB_SLAM2::MapPoint*> vMPs = mpSLAM->GetTrackedMapPoints();
vector<cv::KeyPoint> vKeys = mpSLAM->GetTrackedKeyPointsUn();
cv::undistort(im,imu,K,DistCoef);
if(bRGB)
viewerAR.SetImagePose(imu,Tcw,state,vKeys,vMPs);
else
{
cv::cvtColor(imu,imu,CV_RGB2BGR);
viewerAR.SetImagePose(imu,Tcw,state,vKeys,vMPs);
}
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<ros/ros.h>
#include <cv_bridge/cv_bridge.h>
#include<opencv2/core/core.hpp>
#include"../../../include/System.h"
using namespace std;
class ImageGrabber
{
public:
ImageGrabber(ORB_SLAM2::System* pSLAM):mpSLAM(pSLAM){}
void GrabImage(const sensor_msgs::ImageConstPtr& msg);
ORB_SLAM2::System* mpSLAM;
};
int main(int argc, char **argv)
{
ros::init(argc, argv, "Mono");
ros::start();
if(argc != 3)
{
cerr << endl << "Usage: rosrun ORB_SLAM2 Mono path_to_vocabulary path_to_settings" << endl;
ros::shutdown();
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,true);
ImageGrabber igb(&SLAM);
ros::NodeHandle nodeHandler;
ros::Subscriber sub = nodeHandler.subscribe("/camera/image_raw", 1, &ImageGrabber::GrabImage,&igb);
ros::spin();
// Stop all threads
SLAM.Shutdown();
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
ros::shutdown();
return 0;
}
void ImageGrabber::GrabImage(const sensor_msgs::ImageConstPtr& msg)
{
// Copy the ros image message to cv::Mat.
cv_bridge::CvImageConstPtr cv_ptr;
try
{
cv_ptr = cv_bridge::toCvShare(msg);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
mpSLAM->TrackMonocular(cv_ptr->image,cv_ptr->header.stamp.toSec());
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<ros/ros.h>
#include <cv_bridge/cv_bridge.h>
#include <message_filters/subscriber.h>
#include <message_filters/time_synchronizer.h>
#include <message_filters/sync_policies/approximate_time.h>
#include<opencv2/core/core.hpp>
#include"../../../include/System.h"
using namespace std;
class ImageGrabber
{
public:
ImageGrabber(ORB_SLAM2::System* pSLAM):mpSLAM(pSLAM){}
void GrabRGBD(const sensor_msgs::ImageConstPtr& msgRGB,const sensor_msgs::ImageConstPtr& msgD);
ORB_SLAM2::System* mpSLAM;
};
int main(int argc, char **argv)
{
ros::init(argc, argv, "RGBD");
ros::start();
if(argc != 3)
{
cerr << endl << "Usage: rosrun ORB_SLAM2 RGBD path_to_vocabulary path_to_settings" << endl;
ros::shutdown();
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::RGBD,true);
ImageGrabber igb(&SLAM);
ros::NodeHandle nh;
message_filters::Subscriber<sensor_msgs::Image> rgb_sub(nh, "/camera/rgb/image_raw", 1);
message_filters::Subscriber<sensor_msgs::Image> depth_sub(nh, "camera/depth_registered/image_raw", 1);
typedef message_filters::sync_policies::ApproximateTime<sensor_msgs::Image, sensor_msgs::Image> sync_pol;
message_filters::Synchronizer<sync_pol> sync(sync_pol(10), rgb_sub,depth_sub);
sync.registerCallback(boost::bind(&ImageGrabber::GrabRGBD,&igb,_1,_2));
ros::spin();
// Stop all threads
SLAM.Shutdown();
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
ros::shutdown();
return 0;
}
void ImageGrabber::GrabRGBD(const sensor_msgs::ImageConstPtr& msgRGB,const sensor_msgs::ImageConstPtr& msgD)
{
// Copy the ros image message to cv::Mat.
cv_bridge::CvImageConstPtr cv_ptrRGB;
try
{
cv_ptrRGB = cv_bridge::toCvShare(msgRGB);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
cv_bridge::CvImageConstPtr cv_ptrD;
try
{
cv_ptrD = cv_bridge::toCvShare(msgD);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
mpSLAM->TrackRGBD(cv_ptrRGB->image,cv_ptrD->image,cv_ptrRGB->header.stamp.toSec());
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<ros/ros.h>
#include <cv_bridge/cv_bridge.h>
#include <message_filters/subscriber.h>
#include <message_filters/time_synchronizer.h>
#include <message_filters/sync_policies/approximate_time.h>
#include<opencv2/core/core.hpp>
#include"../../../include/System.h"
using namespace std;
class ImageGrabber
{
public:
ImageGrabber(ORB_SLAM2::System* pSLAM):mpSLAM(pSLAM){}
void GrabStereo(const sensor_msgs::ImageConstPtr& msgLeft,const sensor_msgs::ImageConstPtr& msgRight);
ORB_SLAM2::System* mpSLAM;
bool do_rectify;
cv::Mat M1l,M2l,M1r,M2r;
};
int main(int argc, char **argv)
{
ros::init(argc, argv, "RGBD");
ros::start();
if(argc != 4)
{
cerr << endl << "Usage: rosrun ORB_SLAM2 Stereo path_to_vocabulary path_to_settings do_rectify" << endl;
ros::shutdown();
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::STEREO,true);
ImageGrabber igb(&SLAM);
stringstream ss(argv[3]);
ss >> boolalpha >> igb.do_rectify;
if(igb.do_rectify)
{
// Load settings related to stereo calibration
cv::FileStorage fsSettings(argv[2], cv::FileStorage::READ);
if(!fsSettings.isOpened())
{
cerr << "ERROR: Wrong path to settings" << endl;
return -1;
}
cv::Mat K_l, K_r, P_l, P_r, R_l, R_r, D_l, D_r;
fsSettings["LEFT.K"] >> K_l;
fsSettings["RIGHT.K"] >> K_r;
fsSettings["LEFT.P"] >> P_l;
fsSettings["RIGHT.P"] >> P_r;
fsSettings["LEFT.R"] >> R_l;
fsSettings["RIGHT.R"] >> R_r;
fsSettings["LEFT.D"] >> D_l;
fsSettings["RIGHT.D"] >> D_r;
int rows_l = fsSettings["LEFT.height"];
int cols_l = fsSettings["LEFT.width"];
int rows_r = fsSettings["RIGHT.height"];
int cols_r = fsSettings["RIGHT.width"];
if(K_l.empty() || K_r.empty() || P_l.empty() || P_r.empty() || R_l.empty() || R_r.empty() || D_l.empty() || D_r.empty() ||
rows_l==0 || rows_r==0 || cols_l==0 || cols_r==0)
{
cerr << "ERROR: Calibration parameters to rectify stereo are missing!" << endl;
return -1;
}
cv::initUndistortRectifyMap(K_l,D_l,R_l,P_l.rowRange(0,3).colRange(0,3),cv::Size(cols_l,rows_l),CV_32F,igb.M1l,igb.M2l);
cv::initUndistortRectifyMap(K_r,D_r,R_r,P_r.rowRange(0,3).colRange(0,3),cv::Size(cols_r,rows_r),CV_32F,igb.M1r,igb.M2r);
}
ros::NodeHandle nh;
message_filters::Subscriber<sensor_msgs::Image> left_sub(nh, "/camera/left/image_raw", 1);
message_filters::Subscriber<sensor_msgs::Image> right_sub(nh, "camera/right/image_raw", 1);
typedef message_filters::sync_policies::ApproximateTime<sensor_msgs::Image, sensor_msgs::Image> sync_pol;
message_filters::Synchronizer<sync_pol> sync(sync_pol(10), left_sub,right_sub);
sync.registerCallback(boost::bind(&ImageGrabber::GrabStereo,&igb,_1,_2));
ros::spin();
// Stop all threads
SLAM.Shutdown();
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory_TUM_Format.txt");
SLAM.SaveTrajectoryTUM("FrameTrajectory_TUM_Format.txt");
SLAM.SaveTrajectoryKITTI("FrameTrajectory_KITTI_Format.txt");
ros::shutdown();
return 0;
}
void ImageGrabber::GrabStereo(const sensor_msgs::ImageConstPtr& msgLeft,const sensor_msgs::ImageConstPtr& msgRight)
{
// Copy the ros image message to cv::Mat.
cv_bridge::CvImageConstPtr cv_ptrLeft;
try
{
cv_ptrLeft = cv_bridge::toCvShare(msgLeft);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
cv_bridge::CvImageConstPtr cv_ptrRight;
try
{
cv_ptrRight = cv_bridge::toCvShare(msgRight);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
if(do_rectify)
{
cv::Mat imLeft, imRight;
cv::remap(cv_ptrLeft->image,imLeft,M1l,M2l,cv::INTER_LINEAR);
cv::remap(cv_ptrRight->image,imRight,M1r,M2r,cv::INTER_LINEAR);
mpSLAM->TrackStereo(imLeft,imRight,cv_ptrLeft->header.stamp.toSec());
}
else
{
mpSLAM->TrackStereo(cv_ptrLeft->image,cv_ptrRight->image,cv_ptrLeft->header.stamp.toSec());
}
}

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 435.2046959714599
Camera.fy: 435.2046959714599
Camera.cx: 367.4517211914062
Camera.cy: 252.2008514404297
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 752
Camera.height: 480
# Camera frames per second
Camera.fps: 20.0
# stereo baseline times fx
Camera.bf: 47.90639384423901
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 35
#--------------------------------------------------------------------------------------------
# Stereo Rectification. Only if you need to pre-rectify the images.
# Camera.fx, .fy, etc must be the same as in LEFT.P
#--------------------------------------------------------------------------------------------
LEFT.height: 480
LEFT.width: 752
LEFT.D: !!opencv-matrix
rows: 1
cols: 5
dt: d
data:[-0.28340811, 0.07395907, 0.00019359, 1.76187114e-05, 0.0]
LEFT.K: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [458.654, 0.0, 367.215, 0.0, 457.296, 248.375, 0.0, 0.0, 1.0]
LEFT.R: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [0.999966347530033, -0.001422739138722922, 0.008079580483432283, 0.001365741834644127, 0.9999741760894847, 0.007055629199258132, -0.008089410156878961, -0.007044357138835809, 0.9999424675829176]
LEFT.P: !!opencv-matrix
rows: 3
cols: 4
dt: d
data: [435.2046959714599, 0, 367.4517211914062, 0, 0, 435.2046959714599, 252.2008514404297, 0, 0, 0, 1, 0]
RIGHT.height: 480
RIGHT.width: 752
RIGHT.D: !!opencv-matrix
rows: 1
cols: 5
dt: d
data:[-0.28368365, 0.07451284, -0.00010473, -3.555907e-05, 0.0]
RIGHT.K: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [457.587, 0.0, 379.999, 0.0, 456.134, 255.238, 0.0, 0.0, 1]
RIGHT.R: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [0.9999633526194376, -0.003625811871560086, 0.007755443660172947, 0.003680398547259526, 0.9999684752771629, -0.007035845251224894, -0.007729688520722713, 0.007064130529506649, 0.999945173484644]
RIGHT.P: !!opencv-matrix
rows: 3
cols: 4
dt: d
data: [435.2046959714599, 0, 367.4517211914062, -47.90639384423901, 0, 435.2046959714599, 252.2008514404297, 0, 0, 0, 1, 0]
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1200
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 718.856
Camera.fy: 718.856
Camera.cx: 607.1928
Camera.cy: 185.2157
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 1241
Camera.height: 376
# Camera frames per second
Camera.fps: 10.0
# stereo baseline times fx
Camera.bf: 386.1448
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 35
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.6
Viewer.KeyFrameLineWidth: 4
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.7
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -100
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 721.5377
Camera.fy: 721.5377
Camera.cx: 609.5593
Camera.cy: 172.854
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 1241
Camera.height: 376
# Camera frames per second
Camera.fps: 10.0
# stereo baseline times fx
Camera.bf: 387.5744
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.6
Viewer.KeyFrameLineWidth: 2
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.7
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -100
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 707.0912
Camera.fy: 707.0912
Camera.cx: 601.8873
Camera.cy: 183.1104
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 1241
Camera.height: 376
# Camera frames per second
Camera.fps: 10.0
# stereo baseline times fx
Camera.bf: 379.8145
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 12
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.6
Viewer.KeyFrameLineWidth: 2
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.7
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -100
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<iomanip>
#include<chrono>
#include <unistd.h>
#include<opencv2/core/core.hpp>
#include<System.h>
using namespace std;
void LoadImages(const string &strPathLeft, const string &strPathRight, const string &strPathTimes,
vector<string> &vstrImageLeft, vector<string> &vstrImageRight, vector<double> &vTimeStamps);
int main(int argc, char **argv)
{
if(argc != 6)
{
cerr << endl << "Usage: ./stereo_euroc path_to_vocabulary path_to_settings path_to_left_folder path_to_right_folder path_to_times_file" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageLeft;
vector<string> vstrImageRight;
vector<double> vTimeStamp;
LoadImages(string(argv[3]), string(argv[4]), string(argv[5]), vstrImageLeft, vstrImageRight, vTimeStamp);
if(vstrImageLeft.empty() || vstrImageRight.empty())
{
cerr << "ERROR: No images in provided path." << endl;
return 1;
}
if(vstrImageLeft.size()!=vstrImageRight.size())
{
cerr << "ERROR: Different number of left and right images." << endl;
return 1;
}
// Read rectification parameters
cv::FileStorage fsSettings(argv[2], cv::FileStorage::READ);
if(!fsSettings.isOpened())
{
cerr << "ERROR: Wrong path to settings" << endl;
return -1;
}
cv::Mat K_l, K_r, P_l, P_r, R_l, R_r, D_l, D_r;
fsSettings["LEFT.K"] >> K_l;
fsSettings["RIGHT.K"] >> K_r;
fsSettings["LEFT.P"] >> P_l;
fsSettings["RIGHT.P"] >> P_r;
fsSettings["LEFT.R"] >> R_l;
fsSettings["RIGHT.R"] >> R_r;
fsSettings["LEFT.D"] >> D_l;
fsSettings["RIGHT.D"] >> D_r;
int rows_l = fsSettings["LEFT.height"];
int cols_l = fsSettings["LEFT.width"];
int rows_r = fsSettings["RIGHT.height"];
int cols_r = fsSettings["RIGHT.width"];
if(K_l.empty() || K_r.empty() || P_l.empty() || P_r.empty() || R_l.empty() || R_r.empty() || D_l.empty() || D_r.empty() ||
rows_l==0 || rows_r==0 || cols_l==0 || cols_r==0)
{
cerr << "ERROR: Calibration parameters to rectify stereo are missing!" << endl;
return -1;
}
cv::Mat M1l,M2l,M1r,M2r;
cv::initUndistortRectifyMap(K_l,D_l,R_l,P_l.rowRange(0,3).colRange(0,3),cv::Size(cols_l,rows_l),CV_32F,M1l,M2l);
cv::initUndistortRectifyMap(K_r,D_r,R_r,P_r.rowRange(0,3).colRange(0,3),cv::Size(cols_r,rows_r),CV_32F,M1r,M2r);
const int nImages = vstrImageLeft.size();
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::STEREO,true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl << endl;
// Main loop
cv::Mat imLeft, imRight, imLeftRect, imRightRect;
for(int ni=0; ni<nImages; ni++)
{
// Read left and right images from file
imLeft = cv::imread(vstrImageLeft[ni],CV_LOAD_IMAGE_UNCHANGED);
imRight = cv::imread(vstrImageRight[ni],CV_LOAD_IMAGE_UNCHANGED);
if(imLeft.empty())
{
cerr << endl << "Failed to load image at: "
<< string(vstrImageLeft[ni]) << endl;
return 1;
}
if(imRight.empty())
{
cerr << endl << "Failed to load image at: "
<< string(vstrImageRight[ni]) << endl;
return 1;
}
cv::remap(imLeft,imLeftRect,M1l,M2l,cv::INTER_LINEAR);
cv::remap(imRight,imRightRect,M1r,M2r,cv::INTER_LINEAR);
double tframe = vTimeStamp[ni];
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the images to the SLAM system
SLAM.TrackStereo(imLeftRect,imRightRect,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// Wait to load the next frame
double T=0;
if(ni<nImages-1)
T = vTimeStamp[ni+1]-tframe;
else if(ni>0)
T = tframe-vTimeStamp[ni-1];
if(ttrack<T)
usleep((T-ttrack)*1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(),vTimesTrack.end());
float totaltime = 0;
for(int ni=0; ni<nImages; ni++)
{
totaltime+=vTimesTrack[ni];
}
cout << "-------" << endl << endl;
cout << "median tracking time: " << vTimesTrack[nImages/2] << endl;
cout << "mean tracking time: " << totaltime/nImages << endl;
// Save camera trajectory
SLAM.SaveTrajectoryTUM("CameraTrajectory.txt");
return 0;
}
void LoadImages(const string &strPathLeft, const string &strPathRight, const string &strPathTimes,
vector<string> &vstrImageLeft, vector<string> &vstrImageRight, vector<double> &vTimeStamps)
{
ifstream fTimes;
fTimes.open(strPathTimes.c_str());
vTimeStamps.reserve(5000);
vstrImageLeft.reserve(5000);
vstrImageRight.reserve(5000);
while(!fTimes.eof())
{
string s;
getline(fTimes,s);
if(!s.empty())
{
stringstream ss;
ss << s;
vstrImageLeft.push_back(strPathLeft + "/" + ss.str() + ".png");
vstrImageRight.push_back(strPathRight + "/" + ss.str() + ".png");
double t;
ss >> t;
vTimeStamps.push_back(t/1e9);
}
}
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include <iostream>
#include <algorithm>
#include <fstream>
#include <iomanip>
#include <chrono>
#include <unistd.h>
#include <opencv2/core/core.hpp>
#include <vector>
// #include <pcl/visualization/cloud_viewer.h>
// #include <pcl/io/io.h>
// #include <pcl/io/pcd_io.h>
// #include <pcl/point_types.h>
#include <opencv2/opencv.hpp>
// #include <pcl/features/normal_3d.h>
// #include <pcl/features/principal_curvatures.h>
#include <System.h>
using namespace std;
void LoadLabel(const string &strLabelFilename, vector<vector<double>> &vvLabel);
void LoadImages(const string &strPathToSequence, vector<string> &vstrImageLeft,
vector<string> &vstrImageRight, vector<string> &vstrlabel, vector<double> &vTimestamps);
// pcl::PointCloud<pcl::PointXYZI>::Ptr BEV_GEN(const cv::String &strFile, cv::Mat &BEV_IMG);
int main(int argc, char **argv)
{
if (argc != 6)
{
cerr << endl
<< "Usage: ./stereo_kitti path_to_vocabulary path_to_settings path_to_sequence path_to_RGB path_to_cloud" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageLeft;
vector<string> vstrImageRight;
vector<double> vTimestamps;
vector<string> vstrlabel;
LoadImages(string(argv[3]), vstrImageLeft, vstrImageRight, vstrlabel, vTimestamps);
std::vector<cv::String> fn_cloud, fn_img, fn_label;
cv::String pattern_img = string(argv[4]);
cv::String pattern_cloud = string(argv[5]);
glob(pattern_cloud, fn_cloud, false);
glob(pattern_img, fn_img, false);
cout << "读取成功" << endl;
const int nImages = vstrImageLeft.size();
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1], argv[2], ORB_SLAM2::System::STEREO, true);
SLAM.mpSemantic_Maper->mvpointcloud_adress = fn_cloud;
SLAM.mpSemantic_Maper->mvRGBIMG_adress = fn_img;
// Vector for tracking time statistics
vector<float>
vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl
<< "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl
<< endl;
// Main loop
cv::Mat imLeft, imRight;
cv::String pattern = string(argv[4]);
std::vector<cv::String> fn;
glob(pattern, fn, false);
for (int ni = 0; ni < nImages; ni++)
{
cv::Mat imbev;
// pcl::PointCloud<pcl::PointXYZI>::Ptr tmp; // 保存的原始点云数据，pcl格式
// tmp = BEV_GEN(fn[ni], imbev); // imRGB为BEV视图
vector<vector<double>> vvLabel;
// Read left and right images from file
imLeft = cv::imread(vstrImageLeft[ni], CV_LOAD_IMAGE_UNCHANGED);
imRight = cv::imread(vstrImageRight[ni], CV_LOAD_IMAGE_UNCHANGED);
LoadLabel(vstrlabel[ni], vvLabel);
SLAM.mpSemantic_Maper->mvvvLabel.push_back(vvLabel);
double tframe = vTimestamps[ni];
if (imLeft.empty())
{
cerr << endl
<< "Failed to load image at: "
<< string(vstrImageLeft[ni]) << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the images to the SLAM system
double delta_t = 0.1;
if (ni > 0)
{
delta_t = vTimestamps[ni] - vTimestamps[ni - 1];
}
SLAM.TrackStereo(imLeft, imRight, tframe, vvLabel, imbev, delta_t);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack = std::chrono::duration_cast<std::chrono::duration<double>>(t2 - t1).count();
vTimesTrack[ni] = ttrack;
// Wait to load the next frame
double T = 0;
if (ni < nImages - 1)
T = vTimestamps[ni + 1] - tframe;
else if (ni > 0)
T = tframe - vTimestamps[ni - 1];
if (ttrack < T)
usleep((T - ttrack) * 1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(), vTimesTrack.end());
float totaltime = 0;
for (int ni = 0; ni < nImages; ni++)
{
totaltime += vTimesTrack[ni];
}
cout << "-------" << endl
<< endl;
cout << "median tracking time: " << vTimesTrack[nImages / 2] << endl;
cout << "mean tracking time: " << totaltime / nImages << endl;
// Save camera trajectory
SLAM.SaveTrajectoryKITTI("CameraTrajectory.txt");
return 0;
}
/*
pcl::PointCloud<pcl::PointXYZI>::Ptr BEV_GEN(const cv::String &strFile, cv::Mat &BEV_IMG)
{
int32_t num = 1000000;
float *data = (float *)malloc(num * sizeof(float)); // void *malloc(size_t size) 分配所需的内存空间，并返回一个指向它的指针。
// pointers
float *px = data + 0;
float *py = data + 1;
float *pz = data + 2;
float *pr = data + 3;
// load point cloud
pcl::PointCloud<pcl::PointXYZI>::Ptr point_cloud(new pcl::PointCloud<pcl::PointXYZI>());
FILE *stream;
std::string Filename = strFile;
char ch[200];
strcpy(ch, Filename.c_str());
stream = fopen(ch, "rb");
num = fread(data, sizeof(float), num, stream) / 4;
point_cloud->width = num; // 设定长
point_cloud->height = 1; // 设定高
point_cloud->is_dense = false; // 如果没有无效点（例如，具有NaN或Inf值），则为True
for (int32_t i = 0; i < num; i++)
{
// vector<int32_t> point_cloud;
pcl::PointXYZI point;
point.x = *px;
point.y = *py;
point.z = *pz;
point.intensity = *pr;
point_cloud->points.push_back(point);
px += 4;
py += 4;
pz += 4;
pr += 4;
}
fclose(stream);
free(data); // 释放内存
pcl::PointCloud<pcl::PointXYZI>::Ptr point_cloud_new(new pcl::PointCloud<pcl::PointXYZI>());
for (int i = 0; i < point_cloud->points.size(); i++)
{
if (point_cloud->points[i].x > 0 && point_cloud->points[i].x < 50 && point_cloud->points[i].y > -25 && point_cloud->points[i].y < 25 && point_cloud->points[i].z > -0.8 && point_cloud->points[i].z < 1.3)
{
point_cloud_new->push_back(point_cloud->points[i]);
// cout<<point_cloud_out->points[j].x<<endl;
// j++;
}
}
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_Curvature(new pcl::PointCloud<pcl::PointXYZI>);
pcl::copyPointCloud(*point_cloud_new, *cloud_Curvature); // src中的xyz覆盖掉src_PN中的xyz值，然后把xyz+normal的信息给src_PN
// MaxCurvaturePoints(point_cloud_new, cloud_Curvature); //点云曲率
// point_cloud_new->points.
// 可视化
int Height = 608 + 1;
int Width = (608 + 1);
float Discretization = 50.0 / 608.0;
// pcl::PointCloud<pcl::PointXYZI>::Ptr point_cloud_new_ = *point_cloud_new;
cv::Mat height_map = cv::Mat::zeros(608, 608, CV_8UC1);
cv::Mat intensityMap = cv::Mat::zeros(608, 608, CV_8UC1);
cv::Mat densityMap = cv::Mat::zeros(608, 608, CV_8UC1);
cv::Mat CurvatureMap = cv::Mat::zeros(608, 608, CV_8UC1);
vector<vector<vector<cv::Point3f>>> PointCloud_frac(608, vector<vector<cv::Point3f>>(608, vector<cv::Point3f>()));
for (int i = 0; i < point_cloud_new->points.size(); i++)
{
point_cloud_new->points[i].y = (floor(point_cloud_new->points[i].y / Discretization) + Height / 2);
point_cloud_new->points[i].x = (floor(point_cloud_new->points[i].x / Discretization));
cloud_Curvature->points[i].y = (floor(cloud_Curvature->points[i].y / Discretization) + Height / 2);
cloud_Curvature->points[i].x = (floor(cloud_Curvature->points[i].x / Discretization));
// cout<<"int(point_cloud_new->points[i].x " <<int(point_cloud_new->points[i].x << "int(point_cloud_new->points[i].y " <<int(point_cloud_new->points[i].y <<endl;
cv::Point3f p;
p.x = point_cloud_new->points[i].z + 1.2;
p.y = point_cloud_new->points[i].intensity;
// p.z = cloud_Curvature->points[i].intensity; //曲率
p.z = cloud_Curvature->points[i].intensity;
PointCloud_frac[int(point_cloud_new->points[i].x)][int(point_cloud_new->points[i].y)].push_back(p);
}
// for(int i =0;i<608;i++)
// {
// for(int j =0;j<1218;j++)
// // cout<<"poincloud _size " << PointCloud_frac[i][j].size()<<endl;
// }
for (int i = 0; i < 608; i++)
{
for (int j = 0; j < 608; j++)
{
if (PointCloud_frac[i][j].size() > 0)
{
vector<float> temp_h, temp_I, temp_c;
for (int k = 0; k < PointCloud_frac[i][j].size(); k++)
{
temp_h.push_back(PointCloud_frac[i][j][k].x);
temp_I.push_back(PointCloud_frac[i][j][k].y);
temp_c.push_back(PointCloud_frac[i][j][k].z);
}
height_map.at<uchar>(i, j) = (int)(*max_element(temp_h.begin(), temp_h.end()) / 2.1 * 255.0);
intensityMap.at<uchar>(i, j) = (int)(*max_element(temp_I.begin(), temp_I.end()) * 255.0);
densityMap.at<uchar>(i, j) = (int)(min(1.0, log10(temp_h.size() + 1) / log10(64)) * 255.0);
CurvatureMap.at<uchar>(i, j) = (int)min(255.0, (10000 * (*max_element(temp_c.begin(), temp_c.end())) * 255.0));
}
}
}
vector<cv::Mat> vimg;
vimg.push_back(intensityMap);
vimg.push_back(height_map);
vimg.push_back(densityMap);
// vimg.push_back(intensityMap);
// vimg.push_back(height_map);
// vimg.push_back(CurvatureMap);
cv::merge(vimg, BEV_IMG);
cv::flip(BEV_IMG, BEV_IMG, 0);
cv::flip(BEV_IMG, BEV_IMG, 1);
return point_cloud;
}
*/
void LoadImages(const string &strPathToSequence, vector<string> &vstrImageLeft,
vector<string> &vstrImageRight, vector<string> &vstrlabel, vector<double> &vTimestamps)
{
ifstream fTimes;
string strPathTimeFile = strPathToSequence + "/times.txt";
fTimes.open(strPathTimeFile.c_str());
while (!fTimes.eof())
{
string s;
getline(fTimes, s);
if (!s.empty())
{
stringstream ss;
ss << s;
double t;
ss >> t;
vTimestamps.push_back(t);
}
}
cout << "读取times.txt完成" << endl;
// 用于kitti-odom
string strPrefixLeft = strPathToSequence + "/image_0/";
string strPrefixRight = strPathToSequence + "/image_1/";
// 用于kitti-raw
// string strPrefixLeft = strPathToSequence + "/image_00/";
// string strPrefixRight = strPathToSequence + "/image_01/";
string strlabel = strPathToSequence + "/labels/";
const int nTimes = vTimestamps.size();
vstrImageLeft.resize(nTimes);
vstrImageRight.resize(nTimes);
vstrlabel.resize(nTimes);
for (int i = 0; i < nTimes; i++)
{
stringstream ss;
// 用于kitti-odom
ss << setfill('0') << setw(6) << i;
// 用于kitti-raw
// ss << setfill('0') << setw(10) << i;
vstrImageLeft[i] = strPrefixLeft + ss.str() + ".png";
vstrImageRight[i] = strPrefixRight + ss.str() + ".png";
// vstrlabel[i] = strlabel + ss.str() + ".txt";
}
for (int i = 0; i < nTimes; i++)
{
stringstream ss;
ss << setfill('0') << setw(6) << i;
vstrlabel[i] = strlabel + ss.str() + ".txt";
}
/*for (int i = 0; i < nTimes; i++)
{
stringstream ss;
ss << setfill('0') << setw(6) << i;
vstrlabel[i] = strlabel + ss.str() + ".txt";
}*/
}
void LoadLabel(const string &strLabelFilename, vector<vector<double>> &vvLabel)
{
ifstream fAssociation;
fAssociation.open(strLabelFilename.c_str());
while (!fAssociation.eof())
{
string s;
getline(fAssociation, s);
if (!s.empty())
{
stringstream ss;
ss << s;
double cls, x, y, w, h, im, re, yaw;
vector<double> label;
ss >> cls;
label.push_back(cls);
ss >> x;
x = 608 - x;
label.push_back(x);
ss >> y;
y = 608 - y;
label.push_back(y);
ss >> w;
label.push_back(w);
ss >> h;
label.push_back(h);
ss >> im;
label.push_back(im);
ss >> re;
label.push_back(re);
ss >> yaw;
label.push_back(yaw);
vvLabel.push_back(label);
}
}
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include <iostream>
#include <algorithm>
#include <fstream>
#include <iomanip>
#include <chrono>
#include <unistd.h>
#include <opencv2/core/core.hpp>
#include <vector>
// #include <pcl/visualization/cloud_viewer.h>
// #include <pcl/io/io.h>
// #include <pcl/io/pcd_io.h>
// #include <pcl/point_types.h>
#include <opencv2/opencv.hpp>
// #include <pcl/features/normal_3d.h>
// #include <pcl/features/principal_curvatures.h>
#include <System.h>
using namespace std;
void LoadLabel(const string &strLabelFilename, vector<vector<double>> &vvLabel);
void LoadImages(const string &strPathToSequence, vector<string> &vstrImageLeft,
vector<string> &vstrImageRight, vector<string> &vstrlabel, vector<double> &vTimestamps);
// pcl::PointCloud<pcl::PointXYZI>::Ptr BEV_GEN(const cv::String &strFile, cv::Mat &BEV_IMG);
int main(int argc, char **argv)
{
if (argc != 5)
{
cerr << endl
<< "Usage: ./stereo_kitti path_to_vocabulary path_to_settings path_to_sequence path_to_cloud" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageLeft;
vector<string> vstrImageRight;
vector<double> vTimestamps;
vector<string> vstrlabel;
LoadImages(string(argv[3]), vstrImageLeft, vstrImageRight, vstrlabel, vTimestamps);
cout << "读取成功" << endl;
const int nImages = vstrImageLeft.size();
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1], argv[2], ORB_SLAM2::System::STEREO, true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl
<< "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl
<< endl;
// Main loop
cv::Mat imLeft, imRight;
cv::String pattern = string(argv[4]);
std::vector<cv::String> fn;
glob(pattern, fn, false);
for (int ni = 0; ni < nImages; ni++)
{
cv::Mat imbev;
// pcl::PointCloud<pcl::PointXYZI>::Ptr tmp; // 保存的原始点云数据，pcl格式
// tmp = BEV_GEN(fn[ni], imbev); // imRGB为BEV视图
vector<vector<double>> vvLabel;
// Read left and right images from file
imLeft = cv::imread(vstrImageLeft[ni], CV_LOAD_IMAGE_UNCHANGED);
imRight = cv::imread(vstrImageRight[ni], CV_LOAD_IMAGE_UNCHANGED);
LoadLabel(vstrlabel[ni], vvLabel);
double tframe = vTimestamps[ni];
if (imLeft.empty())
{
cerr << endl
<< "Failed to load image at: "
<< string(vstrImageLeft[ni]) << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the images to the SLAM system
double delta_t = 0.1;
if (ni > 0)
{
delta_t = vTimestamps[ni] - vTimestamps[ni - 1];
}
SLAM.TrackStereo(imLeft, imRight, tframe, vvLabel, imbev, delta_t);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack = std::chrono::duration_cast<std::chrono::duration<double>>(t2 - t1).count();
vTimesTrack[ni] = ttrack;
// Wait to load the next frame
double T = 0;
if (ni < nImages - 1)
T = vTimestamps[ni + 1] - tframe;
else if (ni > 0)
T = tframe - vTimestamps[ni - 1];
if (ttrack < T)
usleep((T - ttrack) * 1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(), vTimesTrack.end());
float totaltime = 0;
for (int ni = 0; ni < nImages; ni++)
{
totaltime += vTimesTrack[ni];
}
cout << "-------" << endl
<< endl;
cout << "median tracking time: " << vTimesTrack[nImages / 2] << endl;
cout << "mean tracking time: " << totaltime / nImages << endl;
// Save camera trajectory
SLAM.SaveTrajectoryKITTI("CameraTrajectory.txt");
return 0;
}
/*
pcl::PointCloud<pcl::PointXYZI>::Ptr BEV_GEN(const cv::String &strFile, cv::Mat &BEV_IMG)
{
int32_t num = 1000000;
float *data = (float *)malloc(num * sizeof(float)); // void *malloc(size_t size) 分配所需的内存空间，并返回一个指向它的指针。
// pointers
float *px = data + 0;
float *py = data + 1;
float *pz = data + 2;
float *pr = data + 3;
// load point cloud
pcl::PointCloud<pcl::PointXYZI>::Ptr point_cloud(new pcl::PointCloud<pcl::PointXYZI>());
FILE *stream;
std::string Filename = strFile;
char ch[200];
strcpy(ch, Filename.c_str());
stream = fopen(ch, "rb");
num = fread(data, sizeof(float), num, stream) / 4;
point_cloud->width = num; // 设定长
point_cloud->height = 1; // 设定高
point_cloud->is_dense = false; // 如果没有无效点（例如，具有NaN或Inf值），则为True
for (int32_t i = 0; i < num; i++)
{
// vector<int32_t> point_cloud;
pcl::PointXYZI point;
point.x = *px;
point.y = *py;
point.z = *pz;
point.intensity = *pr;
point_cloud->points.push_back(point);
px += 4;
py += 4;
pz += 4;
pr += 4;
}
fclose(stream);
free(data); // 释放内存
pcl::PointCloud<pcl::PointXYZI>::Ptr point_cloud_new(new pcl::PointCloud<pcl::PointXYZI>());
for (int i = 0; i < point_cloud->points.size(); i++)
{
if (point_cloud->points[i].x > 0 && point_cloud->points[i].x < 50 && point_cloud->points[i].y > -25 && point_cloud->points[i].y < 25 && point_cloud->points[i].z > -0.8 && point_cloud->points[i].z < 1.3)
{
point_cloud_new->push_back(point_cloud->points[i]);
// cout<<point_cloud_out->points[j].x<<endl;
// j++;
}
}
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_Curvature(new pcl::PointCloud<pcl::PointXYZI>);
pcl::copyPointCloud(*point_cloud_new, *cloud_Curvature); // src中的xyz覆盖掉src_PN中的xyz值，然后把xyz+normal的信息给src_PN
// MaxCurvaturePoints(point_cloud_new, cloud_Curvature); //点云曲率
// point_cloud_new->points.
// 可视化
int Height = 608 + 1;
int Width = (608 + 1);
float Discretization = 50.0 / 608.0;
// pcl::PointCloud<pcl::PointXYZI>::Ptr point_cloud_new_ = *point_cloud_new;
cv::Mat height_map = cv::Mat::zeros(608, 608, CV_8UC1);
cv::Mat intensityMap = cv::Mat::zeros(608, 608, CV_8UC1);
cv::Mat densityMap = cv::Mat::zeros(608, 608, CV_8UC1);
cv::Mat CurvatureMap = cv::Mat::zeros(608, 608, CV_8UC1);
vector<vector<vector<cv::Point3f>>> PointCloud_frac(608, vector<vector<cv::Point3f>>(608, vector<cv::Point3f>()));
for (int i = 0; i < point_cloud_new->points.size(); i++)
{
point_cloud_new->points[i].y = (floor(point_cloud_new->points[i].y / Discretization) + Height / 2);
point_cloud_new->points[i].x = (floor(point_cloud_new->points[i].x / Discretization));
cloud_Curvature->points[i].y = (floor(cloud_Curvature->points[i].y / Discretization) + Height / 2);
cloud_Curvature->points[i].x = (floor(cloud_Curvature->points[i].x / Discretization));
// cout<<"int(point_cloud_new->points[i].x " <<int(point_cloud_new->points[i].x << "int(point_cloud_new->points[i].y " <<int(point_cloud_new->points[i].y <<endl;
cv::Point3f p;
p.x = point_cloud_new->points[i].z + 1.2;
p.y = point_cloud_new->points[i].intensity;
// p.z = cloud_Curvature->points[i].intensity; //曲率
p.z = cloud_Curvature->points[i].intensity;
PointCloud_frac[int(point_cloud_new->points[i].x)][int(point_cloud_new->points[i].y)].push_back(p);
}
// for(int i =0;i<608;i++)
// {
// for(int j =0;j<1218;j++)
// // cout<<"poincloud _size " << PointCloud_frac[i][j].size()<<endl;
// }
for (int i = 0; i < 608; i++)
{
for (int j = 0; j < 608; j++)
{
if (PointCloud_frac[i][j].size() > 0)
{
vector<float> temp_h, temp_I, temp_c;
for (int k = 0; k < PointCloud_frac[i][j].size(); k++)
{
temp_h.push_back(PointCloud_frac[i][j][k].x);
temp_I.push_back(PointCloud_frac[i][j][k].y);
temp_c.push_back(PointCloud_frac[i][j][k].z);
}
height_map.at<uchar>(i, j) = (int)(*max_element(temp_h.begin(), temp_h.end()) / 2.1 * 255.0);
intensityMap.at<uchar>(i, j) = (int)(*max_element(temp_I.begin(), temp_I.end()) * 255.0);
densityMap.at<uchar>(i, j) = (int)(min(1.0, log10(temp_h.size() + 1) / log10(64)) * 255.0);
CurvatureMap.at<uchar>(i, j) = (int)min(255.0, (10000 * (*max_element(temp_c.begin(), temp_c.end())) * 255.0));
}
}
}
vector<cv::Mat> vimg;
vimg.push_back(intensityMap);
vimg.push_back(height_map);
vimg.push_back(densityMap);
// vimg.push_back(intensityMap);
// vimg.push_back(height_map);
// vimg.push_back(CurvatureMap);
cv::merge(vimg, BEV_IMG);
cv::flip(BEV_IMG, BEV_IMG, 0);
cv::flip(BEV_IMG, BEV_IMG, 1);
return point_cloud;
}
*/
void LoadImages(const string &strPathToSequence, vector<string> &vstrImageLeft,
vector<string> &vstrImageRight, vector<string> &vstrlabel, vector<double> &vTimestamps)
{
ifstream fTimes;
string strPathTimeFile = strPathToSequence + "/times.txt";
fTimes.open(strPathTimeFile.c_str());
while (!fTimes.eof())
{
string s;
getline(fTimes, s);
if (!s.empty())
{
stringstream ss;
ss << s;
double t;
ss >> t;
vTimestamps.push_back(t);
}
}
cout << "读取times.txt完成" << endl;
string strPrefixLeft = strPathToSequence + "/image_00/";
string strPrefixRight = strPathToSequence + "/image_01/";
string strlabel = strPathToSequence + "/labels/";
const int nTimes = vTimestamps.size();
vstrImageLeft.resize(nTimes);
vstrImageRight.resize(nTimes);
vstrlabel.resize(nTimes);
for (int i = 0; i < nTimes; i++)
{
stringstream ss;
ss << setfill('0') << setw(10) << i;
vstrImageLeft[i] = strPrefixLeft + ss.str() + ".png";
vstrImageRight[i] = strPrefixRight + ss.str() + ".png";
// vstrlabel[i] = strlabel + ss.str() + ".txt";
}
for (int i = 0; i < nTimes; i++)
{
stringstream ss;
ss << setfill('0') << setw(6) << i;
vstrlabel[i] = strlabel + ss.str() + ".txt";
}
}
void LoadLabel(const string &strLabelFilename, vector<vector<double>> &vvLabel)
{
ifstream fAssociation;
fAssociation.open(strLabelFilename.c_str());
while (!fAssociation.eof())
{
string s;
getline(fAssociation, s);
if (!s.empty())
{
stringstream ss;
ss << s;
double cls, x, y, w, h, im, re, yaw;
vector<double> label;
ss >> cls;
label.push_back(cls);
ss >> x;
x = 608 - x;
label.push_back(x);
ss >> y;
y = 608 - y;
label.push_back(y);
ss >> w;
label.push_back(w);
ss >> h;
label.push_back(h);
ss >> im;
label.push_back(im);
ss >> re;
label.push_back(re);
ss >> yaw;
label.push_back(yaw);
vvLabel.push_back(label);
}
}
}

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结合三维目标检测的MOT的物体级SLAM方法。
目前版本：
三维目标检测结果通过label.txt完成
complex-YOLO给出三维目标检测结果
# ORB-SLAM2
**Authors:** [Raul Mur-Artal](http://webdiis.unizar.es/~raulmur/), [Juan D. Tardos](http://webdiis.unizar.es/~jdtardos/), [J. M. M. Montiel](http://webdiis.unizar.es/~josemari/) and [Dorian Galvez-Lopez](http://doriangalvez.com/) ([DBoW2](https://github.com/dorian3d/DBoW2))
**13 Jan 2017**: OpenCV 3 and Eigen 3.3 are now supported.
**22 Dec 2016**: Added AR demo (see section 7).
ORB-SLAM2 is a real-time SLAM library for **Monocular**, **Stereo** and **RGB-D** cameras that computes the camera trajectory and a sparse 3D reconstruction (in the stereo and RGB-D case with true scale). It is able to detect loops and relocalize the camera in real time. We provide examples to run the SLAM system in the [KITTI dataset](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) as stereo or monocular, in the [TUM dataset](http://vision.in.tum.de/data/datasets/rgbd-dataset) as RGB-D or monocular, and in the [EuRoC dataset](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) as stereo or monocular. We also provide a ROS node to process live monocular, stereo or RGB-D streams. **The library can be compiled without ROS**. ORB-SLAM2 provides a GUI to change between a *SLAM Mode* and *Localization Mode*, see section 9 of this document.
<a href="https://www.youtube.com/embed/ufvPS5wJAx0" target="_blank"><img src="http://img.youtube.com/vi/ufvPS5wJAx0/0.jpg"
alt="ORB-SLAM2" width="240" height="180" border="10" /></a>
<a href="https://www.youtube.com/embed/T-9PYCKhDLM" target="_blank"><img src="http://img.youtube.com/vi/T-9PYCKhDLM/0.jpg"
alt="ORB-SLAM2" width="240" height="180" border="10" /></a>
<a href="https://www.youtube.com/embed/kPwy8yA4CKM" target="_blank"><img src="http://img.youtube.com/vi/kPwy8yA4CKM/0.jpg"
alt="ORB-SLAM2" width="240" height="180" border="10" /></a>
### Related Publications:
[Monocular] Raúl Mur-Artal, J. M. M. Montiel and Juan D. Tardós. **ORB-SLAM: A Versatile and Accurate Monocular SLAM System**. *IEEE Transactions on Robotics,* vol. 31, no. 5, pp. 1147-1163, 2015. (**2015 IEEE Transactions on Robotics Best Paper Award**). **[PDF](http://webdiis.unizar.es/~raulmur/MurMontielTardosTRO15.pdf)**.
[Stereo and RGB-D] Raúl Mur-Artal and Juan D. Tardós. **ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras**. *IEEE Transactions on Robotics,* vol. 33, no. 5, pp. 1255-1262, 2017. **[PDF](https://128.84.21.199/pdf/1610.06475.pdf)**.
[DBoW2 Place Recognizer] Dorian Gálvez-López and Juan D. Tardós. **Bags of Binary Words for Fast Place Recognition in Image Sequences**. *IEEE Transactions on Robotics,* vol. 28, no. 5, pp. 1188-1197, 2012. **[PDF](http://doriangalvez.com/php/dl.php?dlp=GalvezTRO12.pdf)**
# 1. License
ORB-SLAM2 is released under a [GPLv3 license](https://github.com/raulmur/ORB_SLAM2/blob/master/License-gpl.txt). For a list of all code/library dependencies (and associated licenses), please see [Dependencies.md](https://github.com/raulmur/ORB_SLAM2/blob/master/Dependencies.md).
For a closed-source version of ORB-SLAM2 for commercial purposes, please contact the authors: orbslam (at) unizar (dot) es.
If you use ORB-SLAM2 (Monocular) in an academic work, please cite:
@article{murTRO2015,
title={{ORB-SLAM}: a Versatile and Accurate Monocular {SLAM} System},
author={Mur-Artal, Ra\'ul, Montiel, J. M. M. and Tard\'os, Juan D.},
journal={IEEE Transactions on Robotics},
volume={31},
number={5},
pages={1147--1163},
doi = {10.1109/TRO.2015.2463671},
year={2015}
}
if you use ORB-SLAM2 (Stereo or RGB-D) in an academic work, please cite:
@article{murORB2,
title={{ORB-SLAM2}: an Open-Source {SLAM} System for Monocular, Stereo and {RGB-D} Cameras},
author={Mur-Artal, Ra\'ul and Tard\'os, Juan D.},
journal={IEEE Transactions on Robotics},
volume={33},
number={5},
pages={1255--1262},
doi = {10.1109/TRO.2017.2705103},
year={2017}
}
# 2. Prerequisites
We have tested the library in **Ubuntu 12.04**, **14.04** and **16.04**, but it should be easy to compile in other platforms. A powerful computer (e.g. i7) will ensure real-time performance and provide more stable and accurate results.
## C++11 or C++0x Compiler
We use the new thread and chrono functionalities of C++11.
## Pangolin
We use [Pangolin](https://github.com/stevenlovegrove/Pangolin) for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.
## OpenCV
We use [OpenCV](http://opencv.org) to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. **Required at leat 2.4.3. Tested with OpenCV 2.4.11 and OpenCV 3.2**.
## Eigen3
Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. **Required at least 3.1.0**.
## DBoW2 and g2o (Included in Thirdparty folder)
We use modified versions of the [DBoW2](https://github.com/dorian3d/DBoW2) library to perform place recognition and [g2o](https://github.com/RainerKuemmerle/g2o) library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the *Thirdparty* folder.
## ROS (optional)
We provide some examples to process the live input of a monocular, stereo or RGB-D camera using [ROS](ros.org). Building these examples is optional. In case you want to use ROS, a version Hydro or newer is needed.
# 3. Building ORB-SLAM2 library and examples
Clone the repository:
```
git clone https://github.com/raulmur/ORB_SLAM2.git ORB_SLAM2
```
We provide a script `build.sh` to build the *Thirdparty* libraries and *ORB-SLAM2*. Please make sure you have installed all required dependencies (see section 2). Execute:
```
cd ORB_SLAM2
chmod +x build.sh
./build.sh
```
This will create **libORB_SLAM2.so** at *lib* folder and the executables **mono_tum**, **mono_kitti**, **rgbd_tum**, **stereo_kitti**, **mono_euroc** and **stereo_euroc** in *Examples* folder.
# 4. Monocular Examples
## TUM Dataset
1. Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.
2. Execute the following command. Change `TUMX.yaml` to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change `PATH_TO_SEQUENCE_FOLDER`to the uncompressed sequence folder.
```
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER
```
## KITTI Dataset
1. Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php
2. Execute the following command. Change `KITTIX.yaml`by KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change `PATH_TO_DATASET_FOLDER` to the uncompressed dataset folder. Change `SEQUENCE_NUMBER` to 00, 01, 02,.., 11.
```
./Examples/Monocular/mono_kitti Vocabulary/ORBvoc.txt Examples/Monocular/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER
```
## EuRoC Dataset
1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
2. Execute the following first command for V1 and V2 sequences, or the second command for MH sequences. Change PATH_TO_SEQUENCE_FOLDER and SEQUENCE according to the sequence you want to run.
```
./Examples/Monocular/mono_euroc Vocabulary/ORBvoc.txt Examples/Monocular/EuRoC.yaml PATH_TO_SEQUENCE_FOLDER/mav0/cam0/data Examples/Monocular/EuRoC_TimeStamps/SEQUENCE.txt
```
```
./Examples/Monocular/mono_euroc Vocabulary/ORBvoc.txt Examples/Monocular/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data Examples/Monocular/EuRoC_TimeStamps/SEQUENCE.txt
```
# 5. Stereo Examples
## KITTI Dataset
1. Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php
2. Execute the following command. Change `KITTIX.yaml`to KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change `PATH_TO_DATASET_FOLDER` to the uncompressed dataset folder. Change `SEQUENCE_NUMBER` to 00, 01, 02,.., 11.
```
./Examples/Stereo/stereo_kitti Vocabulary/ORBvoc.txt Examples/Stereo/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER
```
## EuRoC Dataset
1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
2. Execute the following first command for V1 and V2 sequences, or the second command for MH sequences. Change PATH_TO_SEQUENCE_FOLDER and SEQUENCE according to the sequence you want to run.
```
./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/mav0/cam0/data PATH_TO_SEQUENCE/mav0/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt
```
```
./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data PATH_TO_SEQUENCE/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt
```
# 6. RGB-D Example
## TUM Dataset
1. Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.
2. Associate RGB images and depth images using the python script [associate.py](http://vision.in.tum.de/data/datasets/rgbd-dataset/tools). We already provide associations for some of the sequences in *Examples/RGB-D/associations/*. You can generate your own associations file executing:
```
python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
```
3. Execute the following command. Change `TUMX.yaml` to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change `PATH_TO_SEQUENCE_FOLDER`to the uncompressed sequence folder. Change `ASSOCIATIONS_FILE` to the path to the corresponding associations file.
```
./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE
```
# 7. ROS Examples
### Building the nodes for mono, monoAR, stereo and RGB-D
1. Add the path including *Examples/ROS/ORB_SLAM2* to the ROS_PACKAGE_PATH environment variable. Open .bashrc file and add at the end the following line. Replace PATH by the folder where you cloned ORB_SLAM2:
```
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM2/Examples/ROS
```
2. Execute `build_ros.sh` script:
```
chmod +x build_ros.sh
./build_ros.sh
```
### Running Monocular Node
For a monocular input from topic `/camera/image_raw` run node ORB_SLAM2/Mono. You will need to provide the vocabulary file and a settings file. See the monocular examples above.
```
rosrun ORB_SLAM2 Mono PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE
```
### Running Monocular Augmented Reality Demo
This is a demo of augmented reality where you can use an interface to insert virtual cubes in planar regions of the scene.
The node reads images from topic `/camera/image_raw`.
```
rosrun ORB_SLAM2 MonoAR PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE
```
### Running Stereo Node
For a stereo input from topic `/camera/left/image_raw` and `/camera/right/image_raw` run node ORB_SLAM2/Stereo. You will need to provide the vocabulary file and a settings file. If you **provide rectification matrices** (see Examples/Stereo/EuRoC.yaml example), the node will recitify the images online, **otherwise images must be pre-rectified**.
```
rosrun ORB_SLAM2 Stereo PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ONLINE_RECTIFICATION
```
**Example**: Download a rosbag (e.g. V1_01_easy.bag) from the EuRoC dataset (http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets). Open 3 tabs on the terminal and run the following command at each tab:
```
roscore
```
```
rosrun ORB_SLAM2 Stereo Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml true
```
```
rosbag play --pause V1_01_easy.bag /cam0/image_raw:=/camera/left/image_raw /cam1/image_raw:=/camera/right/image_raw
```
Once ORB-SLAM2 has loaded the vocabulary, press space in the rosbag tab. Enjoy!. Note: a powerful computer is required to run the most exigent sequences of this dataset.
### Running RGB_D Node
For an RGB-D input from topics `/camera/rgb/image_raw` and `/camera/depth_registered/image_raw`, run node ORB_SLAM2/RGBD. You will need to provide the vocabulary file and a settings file. See the RGB-D example above.
```
rosrun ORB_SLAM2 RGBD PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE
```
# 8. Processing your own sequences
You will need to create a settings file with the calibration of your camera. See the settings file provided for the TUM and KITTI datasets for monocular, stereo and RGB-D cameras. We use the calibration model of OpenCV. See the examples to learn how to create a program that makes use of the ORB-SLAM2 library and how to pass images to the SLAM system. Stereo input must be synchronized and rectified. RGB-D input must be synchronized and depth registered.
# 9. SLAM and Localization Modes
You can change between the *SLAM* and *Localization mode* using the GUI of the map viewer.
### SLAM Mode
This is the default mode. The system runs in parallal three threads: Tracking, Local Mapping and Loop Closing. The system localizes the camera, builds new map and tries to close loops.
### Localization Mode
This mode can be used when you have a good map of your working area. In this mode the Local Mapping and Loop Closing are deactivated. The system localizes the camera in the map (which is no longer updated), using relocalization if needed.

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cmake_minimum_required(VERSION 2.8)
project(DBoW2)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -O3 -march=native ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -march=native")
set(HDRS_DBOW2
DBoW2/BowVector.h
DBoW2/FORB.h
DBoW2/FClass.h
DBoW2/FeatureVector.h
DBoW2/ScoringObject.h
DBoW2/TemplatedVocabulary.h)
set(SRCS_DBOW2
DBoW2/BowVector.cpp
DBoW2/FORB.cpp
DBoW2/FeatureVector.cpp
DBoW2/ScoringObject.cpp)
set(HDRS_DUTILS
DUtils/Random.h
DUtils/Timestamp.h)
set(SRCS_DUTILS
DUtils/Random.cpp
DUtils/Timestamp.cpp)
find_package(OpenCV 3.0 QUIET)
if(NOT OpenCV_FOUND)
find_package(OpenCV 2.4.3 QUIET)
if(NOT OpenCV_FOUND)
message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
endif()
endif()
set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/lib)
include_directories(${OpenCV_INCLUDE_DIRS})
add_library(DBoW2 SHARED ${SRCS_DBOW2} ${SRCS_DUTILS})
target_link_libraries(DBoW2 ${OpenCV_LIBS})

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/**
* File: BowVector.cpp
* Date: March 2011
* Author: Dorian Galvez-Lopez
* Description: bag of words vector
* License: see the LICENSE.txt file
*
*/
#include <iostream>
#include <fstream>
#include <vector>
#include <algorithm>
#include <cmath>
#include "BowVector.h"
namespace DBoW2 {
// --------------------------------------------------------------------------
BowVector::BowVector(void)
{
}
// --------------------------------------------------------------------------
BowVector::~BowVector(void)
{
}
// --------------------------------------------------------------------------
void BowVector::addWeight(WordId id, WordValue v)
{
BowVector::iterator vit = this->lower_bound(id);
if(vit != this->end() && !(this->key_comp()(id, vit->first)))
{
vit->second += v;
}
else
{
this->insert(vit, BowVector::value_type(id, v));
}
}
// --------------------------------------------------------------------------
void BowVector::addIfNotExist(WordId id, WordValue v)
{
BowVector::iterator vit = this->lower_bound(id);
if(vit == this->end() || (this->key_comp()(id, vit->first)))
{
this->insert(vit, BowVector::value_type(id, v));
}
}
// --------------------------------------------------------------------------
void BowVector::normalize(LNorm norm_type)
{
double norm = 0.0;
BowVector::iterator it;
if(norm_type == DBoW2::L1)
{
for(it = begin(); it != end(); ++it)
norm += fabs(it->second);
}
else
{
for(it = begin(); it != end(); ++it)
norm += it->second * it->second;
norm = sqrt(norm);
}
if(norm > 0.0)
{
for(it = begin(); it != end(); ++it)
it->second /= norm;
}
}
// --------------------------------------------------------------------------
std::ostream& operator<< (std::ostream &out, const BowVector &v)
{
BowVector::const_iterator vit;
std::vector<unsigned int>::const_iterator iit;
unsigned int i = 0;
const unsigned int N = v.size();
for(vit = v.begin(); vit != v.end(); ++vit, ++i)
{
out << "<" << vit->first << ", " << vit->second << ">";
if(i < N-1) out << ", ";
}
return out;
}
// --------------------------------------------------------------------------
void BowVector::saveM(const std::string &filename, size_t W) const
{
std::fstream f(filename.c_str(), std::ios::out);
WordId last = 0;
BowVector::const_iterator bit;
for(bit = this->begin(); bit != this->end(); ++bit)
{
for(; last < bit->first; ++last)
{
f << "0 ";
}
f << bit->second << " ";
last = bit->first + 1;
}
for(; last < (WordId)W; ++last)
f << "0 ";
f.close();
}
// --------------------------------------------------------------------------
} // namespace DBoW2

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/**
* File: BowVector.h
* Date: March 2011
* Author: Dorian Galvez-Lopez
* Description: bag of words vector
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_BOW_VECTOR__
#define __D_T_BOW_VECTOR__
#include <iostream>
#include <map>
#include <vector>
namespace DBoW2 {
/// Id of words
typedef unsigned int WordId;
/// Value of a word
typedef double WordValue;
/// Id of nodes in the vocabulary treee
typedef unsigned int NodeId;
/// L-norms for normalization
enum LNorm
{
L1,
L2
};
/// Weighting type
enum WeightingType
{
TF_IDF,
TF,
IDF,
BINARY
};
/// Scoring type
enum ScoringType
{
L1_NORM,
L2_NORM,
CHI_SQUARE,
KL,
BHATTACHARYYA,
DOT_PRODUCT,
};
/// Vector of words to represent images
class BowVector:
public std::map<WordId, WordValue>
{
public:
/**
* Constructor
*/
BowVector(void);
/**
* Destructor
*/
~BowVector(void);
/**
* Adds a value to a word value existing in the vector, or creates a new
* word with the given value
* @param id word id to look for
* @param v value to create the word with, or to add to existing word
*/
void addWeight(WordId id, WordValue v);
/**
* Adds a word with a value to the vector only if this does not exist yet
* @param id word id to look for
* @param v value to give to the word if this does not exist
*/
void addIfNotExist(WordId id, WordValue v);
/**
* L1-Normalizes the values in the vector
* @param norm_type norm used
*/
void normalize(LNorm norm_type);
/**
* Prints the content of the bow vector
* @param out stream
* @param v
*/
friend std::ostream& operator<<(std::ostream &out, const BowVector &v);
/**
* Saves the bow vector as a vector in a matlab file
* @param filename
* @param W number of words in the vocabulary
*/
void saveM(const std::string &filename, size_t W) const;
};
} // namespace DBoW2
#endif

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/**
* File: FClass.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: generic FClass to instantiate templated classes
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_FCLASS__
#define __D_T_FCLASS__
#include <opencv2/core/core.hpp>
#include <vector>
#include <string>
namespace DBoW2 {
/// Generic class to encapsulate functions to manage descriptors.
/**
* This class must be inherited. Derived classes can be used as the
* parameter F when creating Templated structures
* (TemplatedVocabulary, TemplatedDatabase, ...)
*/
class FClass
{
class TDescriptor;
typedef const TDescriptor *pDescriptor;
/**
* Calculates the mean value of a set of descriptors
* @param descriptors
* @param mean mean descriptor
*/
virtual void meanValue(const std::vector<pDescriptor> &descriptors,
TDescriptor &mean) = 0;
/**
* Calculates the distance between two descriptors
* @param a
* @param b
* @return distance
*/
static double distance(const TDescriptor &a, const TDescriptor &b);
/**
* Returns a string version of the descriptor
* @param a descriptor
* @return string version
*/
static std::string toString(const TDescriptor &a);
/**
* Returns a descriptor from a string
* @param a descriptor
* @param s string version
*/
static void fromString(TDescriptor &a, const std::string &s);
/**
* Returns a mat with the descriptors in float format
* @param descriptors
* @param mat (out) NxL 32F matrix
*/
static void toMat32F(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat);
};
} // namespace DBoW2
#endif

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/**
* File: FORB.cpp
* Date: June 2012
* Author: Dorian Galvez-Lopez
* Description: functions for ORB descriptors
* License: see the LICENSE.txt file
*
* Distance function has been modified
*
*/
#include <vector>
#include <string>
#include <sstream>
#include <stdint-gcc.h>
#include "FORB.h"
using namespace std;
namespace DBoW2 {
// --------------------------------------------------------------------------
const int FORB::L=32;
void FORB::meanValue(const std::vector<FORB::pDescriptor> &descriptors,
FORB::TDescriptor &mean)
{
if(descriptors.empty())
{
mean.release();
return;
}
else if(descriptors.size() == 1)
{
mean = descriptors[0]->clone();
}
else
{
vector<int> sum(FORB::L * 8, 0);
for(size_t i = 0; i < descriptors.size(); ++i)
{
const cv::Mat &d = *descriptors[i];
const unsigned char *p = d.ptr<unsigned char>();
for(int j = 0; j < d.cols; ++j, ++p)
{
if(*p & (1 << 7)) ++sum[ j*8 ];
if(*p & (1 << 6)) ++sum[ j*8 + 1 ];
if(*p & (1 << 5)) ++sum[ j*8 + 2 ];
if(*p & (1 << 4)) ++sum[ j*8 + 3 ];
if(*p & (1 << 3)) ++sum[ j*8 + 4 ];
if(*p & (1 << 2)) ++sum[ j*8 + 5 ];
if(*p & (1 << 1)) ++sum[ j*8 + 6 ];
if(*p & (1)) ++sum[ j*8 + 7 ];
}
}
mean = cv::Mat::zeros(1, FORB::L, CV_8U);
unsigned char *p = mean.ptr<unsigned char>();
const int N2 = (int)descriptors.size() / 2 + descriptors.size() % 2;
for(size_t i = 0; i < sum.size(); ++i)
{
if(sum[i] >= N2)
{
// set bit
*p |= 1 << (7 - (i % 8));
}
if(i % 8 == 7) ++p;
}
}
}
// --------------------------------------------------------------------------
int FORB::distance(const FORB::TDescriptor &a,
const FORB::TDescriptor &b)
{
// Bit set count operation from
// http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel
const int *pa = a.ptr<int32_t>();
const int *pb = b.ptr<int32_t>();
int dist=0;
for(int i=0; i<8; i++, pa++, pb++)
{
unsigned int v = *pa ^ *pb;
v = v - ((v >> 1) & 0x55555555);
v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
dist += (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24;
}
return dist;
}
// --------------------------------------------------------------------------
std::string FORB::toString(const FORB::TDescriptor &a)
{
stringstream ss;
const unsigned char *p = a.ptr<unsigned char>();
for(int i = 0; i < a.cols; ++i, ++p)
{
ss << (int)*p << " ";
}
return ss.str();
}
// --------------------------------------------------------------------------
void FORB::fromString(FORB::TDescriptor &a, const std::string &s)
{
a.create(1, FORB::L, CV_8U);
unsigned char *p = a.ptr<unsigned char>();
stringstream ss(s);
for(int i = 0; i < FORB::L; ++i, ++p)
{
int n;
ss >> n;
if(!ss.fail())
*p = (unsigned char)n;
}
}
// --------------------------------------------------------------------------
void FORB::toMat32F(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat)
{
if(descriptors.empty())
{
mat.release();
return;
}
const size_t N = descriptors.size();
mat.create(N, FORB::L*8, CV_32F);
float *p = mat.ptr<float>();
for(size_t i = 0; i < N; ++i)
{
const int C = descriptors[i].cols;
const unsigned char *desc = descriptors[i].ptr<unsigned char>();
for(int j = 0; j < C; ++j, p += 8)
{
p[0] = (desc[j] & (1 << 7) ? 1 : 0);
p[1] = (desc[j] & (1 << 6) ? 1 : 0);
p[2] = (desc[j] & (1 << 5) ? 1 : 0);
p[3] = (desc[j] & (1 << 4) ? 1 : 0);
p[4] = (desc[j] & (1 << 3) ? 1 : 0);
p[5] = (desc[j] & (1 << 2) ? 1 : 0);
p[6] = (desc[j] & (1 << 1) ? 1 : 0);
p[7] = desc[j] & (1);
}
}
}
// --------------------------------------------------------------------------
void FORB::toMat8U(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat)
{
mat.create(descriptors.size(), 32, CV_8U);
unsigned char *p = mat.ptr<unsigned char>();
for(size_t i = 0; i < descriptors.size(); ++i, p += 32)
{
const unsigned char *d = descriptors[i].ptr<unsigned char>();
std::copy(d, d+32, p);
}
}
// --------------------------------------------------------------------------
} // namespace DBoW2

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/**
* File: FORB.h
* Date: June 2012
* Author: Dorian Galvez-Lopez
* Description: functions for ORB descriptors
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_F_ORB__
#define __D_T_F_ORB__
#include <opencv2/core/core.hpp>
#include <vector>
#include <string>
#include "FClass.h"
namespace DBoW2 {
/// Functions to manipulate ORB descriptors
class FORB: protected FClass
{
public:
/// Descriptor type
typedef cv::Mat TDescriptor; // CV_8U
/// Pointer to a single descriptor
typedef const TDescriptor *pDescriptor;
/// Descriptor length (in bytes)
static const int L;
/**
* Calculates the mean value of a set of descriptors
* @param descriptors
* @param mean mean descriptor
*/
static void meanValue(const std::vector<pDescriptor> &descriptors,
TDescriptor &mean);
/**
* Calculates the distance between two descriptors
* @param a
* @param b
* @return distance
*/
static int distance(const TDescriptor &a, const TDescriptor &b);
/**
* Returns a string version of the descriptor
* @param a descriptor
* @return string version
*/
static std::string toString(const TDescriptor &a);
/**
* Returns a descriptor from a string
* @param a descriptor
* @param s string version
*/
static void fromString(TDescriptor &a, const std::string &s);
/**
* Returns a mat with the descriptors in float format
* @param descriptors
* @param mat (out) NxL 32F matrix
*/
static void toMat32F(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat);
static void toMat8U(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat);
};
} // namespace DBoW2
#endif

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/**
* File: FeatureVector.cpp
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: feature vector
* License: see the LICENSE.txt file
*
*/
#include "FeatureVector.h"
#include <map>
#include <vector>
#include <iostream>
namespace DBoW2 {
// ---------------------------------------------------------------------------
FeatureVector::FeatureVector(void)
{
}
// ---------------------------------------------------------------------------
FeatureVector::~FeatureVector(void)
{
}
// ---------------------------------------------------------------------------
void FeatureVector::addFeature(NodeId id, unsigned int i_feature)
{
FeatureVector::iterator vit = this->lower_bound(id);
if(vit != this->end() && vit->first == id)
{
vit->second.push_back(i_feature);
}
else
{
vit = this->insert(vit, FeatureVector::value_type(id,
std::vector<unsigned int>() ));
vit->second.push_back(i_feature);
}
}
// ---------------------------------------------------------------------------
std::ostream& operator<<(std::ostream &out,
const FeatureVector &v)
{
if(!v.empty())
{
FeatureVector::const_iterator vit = v.begin();
const std::vector<unsigned int>* f = &vit->second;
out << "<" << vit->first << ": [";
if(!f->empty()) out << (*f)[0];
for(unsigned int i = 1; i < f->size(); ++i)
{
out << ", " << (*f)[i];
}
out << "]>";
for(++vit; vit != v.end(); ++vit)
{
f = &vit->second;
out << ", <" << vit->first << ": [";
if(!f->empty()) out << (*f)[0];
for(unsigned int i = 1; i < f->size(); ++i)
{
out << ", " << (*f)[i];
}
out << "]>";
}
}
return out;
}
// ---------------------------------------------------------------------------
} // namespace DBoW2

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/**
* File: FeatureVector.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: feature vector
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_FEATURE_VECTOR__
#define __D_T_FEATURE_VECTOR__
#include "BowVector.h"
#include <map>
#include <vector>
#include <iostream>
namespace DBoW2 {
/// Vector of nodes with indexes of local features
class FeatureVector:
public std::map<NodeId, std::vector<unsigned int> >
{
public:
/**
* Constructor
*/
FeatureVector(void);
/**
* Destructor
*/
~FeatureVector(void);
/**
* Adds a feature to an existing node, or adds a new node with an initial
* feature
* @param id node id to add or to modify
* @param i_feature index of feature to add to the given node
*/
void addFeature(NodeId id, unsigned int i_feature);
/**
* Sends a string versions of the feature vector through the stream
* @param out stream
* @param v feature vector
*/
friend std::ostream& operator<<(std::ostream &out, const FeatureVector &v);
};
} // namespace DBoW2
#endif

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/**
* File: ScoringObject.cpp
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: functions to compute bow scores
* License: see the LICENSE.txt file
*
*/
#include <cfloat>
#include "TemplatedVocabulary.h"
#include "BowVector.h"
using namespace DBoW2;
// If you change the type of WordValue, make sure you change also the
// epsilon value (this is needed by the KL method)
const double GeneralScoring::LOG_EPS = log(DBL_EPSILON); // FLT_EPSILON
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double L1Scoring::score(const BowVector &v1, const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += fabs(vi - wi) - fabs(vi) - fabs(wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
// ||v - w||_{L1} = 2 + Sum(|v_i - w_i| - |v_i| - |w_i|)
// for all i | v_i != 0 and w_i != 0
// (Nister, 2006)
// scaled_||v - w||_{L1} = 1 - 0.5 * ||v - w||_{L1}
score = -score/2.0;
return score; // [0..1]
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double L2Scoring::score(const BowVector &v1, const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += vi * wi;
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
// ||v - w||_{L2} = sqrt( 2 - 2 * Sum(v_i * w_i) )
// for all i | v_i != 0 and w_i != 0 )
// (Nister, 2006)
if(score >= 1) // rounding errors
score = 1.0;
else
score = 1.0 - sqrt(1.0 - score); // [0..1]
return score;
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double ChiSquareScoring::score(const BowVector &v1, const BowVector &v2)
const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
// all the items are taken into account
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
// (v-w)^2/(v+w) - v - w = -4 vw/(v+w)
// we move the -4 out
if(vi + wi != 0.0) score += vi * wi / (vi + wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
}
}
// this takes the -4 into account
score = 2. * score; // [0..1]
return score;
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double KLScoring::score(const BowVector &v1, const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
// all the items or v are taken into account
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
if(vi != 0 && wi != 0) score += vi * log(vi/wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
score += vi * (log(vi) - LOG_EPS);
++v1_it;
}
else
{
// move v2_it forward, do not add any score
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
// sum rest of items of v
for(; v1_it != v1_end; ++v1_it)
if(v1_it->second != 0)
score += v1_it->second * (log(v1_it->second) - LOG_EPS);
return score; // cannot be scaled
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double BhattacharyyaScoring::score(const BowVector &v1,
const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += sqrt(vi * wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
return score; // already scaled
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double DotProductScoring::score(const BowVector &v1,
const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += vi * wi;
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
return score; // cannot scale
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------

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/**
* File: ScoringObject.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: functions to compute bow scores
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_SCORING_OBJECT__
#define __D_T_SCORING_OBJECT__
#include "BowVector.h"
namespace DBoW2 {
/// Base class of scoring functions
class GeneralScoring
{
public:
/**
* Computes the score between two vectors. Vectors must be sorted and
* normalized if necessary
* @param v (in/out)
* @param w (in/out)
* @return score
*/
virtual double score(const BowVector &v, const BowVector &w) const = 0;
/**
* Returns whether a vector must be normalized before scoring according
* to the scoring scheme
* @param norm norm to use
* @return true iff must normalize
*/
virtual bool mustNormalize(LNorm &norm) const = 0;
/// Log of epsilon
static const double LOG_EPS;
// If you change the type of WordValue, make sure you change also the
// epsilon value (this is needed by the KL method)
virtual ~GeneralScoring() {} //!< Required for virtual base classes
};
/**
* Macro for defining Scoring classes
* @param NAME name of class
* @param MUSTNORMALIZE if vectors must be normalized to compute the score
* @param NORM type of norm to use when MUSTNORMALIZE
*/
#define __SCORING_CLASS(NAME, MUSTNORMALIZE, NORM) \
NAME: public GeneralScoring \
{ public: \
/** \
* Computes score between two vectors \
* @param v \
* @param w \
* @return score between v and w \
*/ \
virtual double score(const BowVector &v, const BowVector &w) const; \
\
/** \
* Says if a vector must be normalized according to the scoring function \
* @param norm (out) if true, norm to use
* @return true iff vectors must be normalized \
*/ \
virtual inline bool mustNormalize(LNorm &norm) const \
{ norm = NORM; return MUSTNORMALIZE; } \
}
/// L1 Scoring object
class __SCORING_CLASS(L1Scoring, true, L1);
/// L2 Scoring object
class __SCORING_CLASS(L2Scoring, true, L2);
/// Chi square Scoring object
class __SCORING_CLASS(ChiSquareScoring, true, L1);
/// KL divergence Scoring object
class __SCORING_CLASS(KLScoring, true, L1);
/// Bhattacharyya Scoring object
class __SCORING_CLASS(BhattacharyyaScoring, true, L1);
/// Dot product Scoring object
class __SCORING_CLASS(DotProductScoring, false, L1);
#undef __SCORING_CLASS
} // namespace DBoW2
#endif

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/*
* File: Random.cpp
* Project: DUtils library
* Author: Dorian Galvez-Lopez
* Date: April 2010
* Description: manages pseudo-random numbers
* License: see the LICENSE.txt file
*
*/
#include "Random.h"
#include "Timestamp.h"
#include <cstdlib>
using namespace std;
bool DUtils::Random::m_already_seeded = false;
void DUtils::Random::SeedRand(){
Timestamp time;
time.setToCurrentTime();
srand((unsigned)time.getFloatTime());
}
void DUtils::Random::SeedRandOnce()
{
if(!m_already_seeded)
{
DUtils::Random::SeedRand();
m_already_seeded = true;
}
}
void DUtils::Random::SeedRand(int seed)
{
srand(seed);
}
void DUtils::Random::SeedRandOnce(int seed)
{
if(!m_already_seeded)
{
DUtils::Random::SeedRand(seed);
m_already_seeded = true;
}
}
int DUtils::Random::RandomInt(int min, int max){
int d = max - min + 1;
return int(((double)rand()/((double)RAND_MAX + 1.0)) * d) + min;
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
DUtils::Random::UnrepeatedRandomizer::UnrepeatedRandomizer(int min, int max)
{
if(min <= max)
{
m_min = min;
m_max = max;
}
else
{
m_min = max;
m_max = min;
}
createValues();
}
// ---------------------------------------------------------------------------
DUtils::Random::UnrepeatedRandomizer::UnrepeatedRandomizer
(const DUtils::Random::UnrepeatedRandomizer& rnd)
{
*this = rnd;
}
// ---------------------------------------------------------------------------
int DUtils::Random::UnrepeatedRandomizer::get()
{
if(empty()) createValues();
DUtils::Random::SeedRandOnce();
int k = DUtils::Random::RandomInt(0, m_values.size()-1);
int ret = m_values[k];
m_values[k] = m_values.back();
m_values.pop_back();
return ret;
}
// ---------------------------------------------------------------------------
void DUtils::Random::UnrepeatedRandomizer::createValues()
{
int n = m_max - m_min + 1;
m_values.resize(n);
for(int i = 0; i < n; ++i) m_values[i] = m_min + i;
}
// ---------------------------------------------------------------------------
void DUtils::Random::UnrepeatedRandomizer::reset()
{
if((int)m_values.size() != m_max - m_min + 1) createValues();
}
// ---------------------------------------------------------------------------
DUtils::Random::UnrepeatedRandomizer&
DUtils::Random::UnrepeatedRandomizer::operator=
(const DUtils::Random::UnrepeatedRandomizer& rnd)
{
if(this != &rnd)
{
this->m_min = rnd.m_min;
this->m_max = rnd.m_max;
this->m_values = rnd.m_values;
}
return *this;
}
// ---------------------------------------------------------------------------

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/*
* File: Random.h
* Project: DUtils library
* Author: Dorian Galvez-Lopez
* Date: April 2010, November 2011
* Description: manages pseudo-random numbers
* License: see the LICENSE.txt file
*
*/
#pragma once
#ifndef __D_RANDOM__
#define __D_RANDOM__
#include <cstdlib>
#include <vector>
namespace DUtils {
/// Functions to generate pseudo-random numbers
class Random
{
public:
class UnrepeatedRandomizer;
public:
/**
* Sets the random number seed to the current time
*/
static void SeedRand();
/**
* Sets the random number seed to the current time only the first
* time this function is called
*/
static void SeedRandOnce();
/**
* Sets the given random number seed
* @param seed
*/
static void SeedRand(int seed);
/**
* Sets the given random number seed only the first time this function
* is called
* @param seed
*/
static void SeedRandOnce(int seed);
/**
* Returns a random number in the range [0..1]
* @return random T number in [0..1]
*/
template <class T>
static T RandomValue(){
return (T)rand()/(T)RAND_MAX;
}
/**
* Returns a random number in the range [min..max]
* @param min
* @param max
* @return random T number in [min..max]
*/
template <class T>
static T RandomValue(T min, T max){
return Random::RandomValue<T>() * (max - min) + min;
}
/**
* Returns a random int in the range [min..max]
* @param min
* @param max
* @return random int in [min..max]
*/
static int RandomInt(int min, int max);
/**
* Returns a random number from a gaussian distribution
* @param mean
* @param sigma standard deviation
*/
template <class T>
static T RandomGaussianValue(T mean, T sigma)
{
// Box-Muller transformation
T x1, x2, w, y1;
do {
x1 = (T)2. * RandomValue<T>() - (T)1.;
x2 = (T)2. * RandomValue<T>() - (T)1.;
w = x1 * x1 + x2 * x2;
} while ( w >= (T)1. || w == (T)0. );
w = sqrt( ((T)-2.0 * log( w ) ) / w );
y1 = x1 * w;
return( mean + y1 * sigma );
}
private:
/// If SeedRandOnce() or SeedRandOnce(int) have already been called
static bool m_already_seeded;
};
// ---------------------------------------------------------------------------
/// Provides pseudo-random numbers with no repetitions
class Random::UnrepeatedRandomizer
{
public:
/**
* Creates a randomizer that returns numbers in the range [min, max]
* @param min
* @param max
*/
UnrepeatedRandomizer(int min, int max);
~UnrepeatedRandomizer(){}
/**
* Copies a randomizer
* @param rnd
*/
UnrepeatedRandomizer(const UnrepeatedRandomizer& rnd);
/**
* Copies a randomizer
* @param rnd
*/
UnrepeatedRandomizer& operator=(const UnrepeatedRandomizer& rnd);
/**
* Returns a random number not given before. If all the possible values
* were already given, the process starts again
* @return unrepeated random number
*/
int get();
/**
* Returns whether all the possible values between min and max were
* already given. If get() is called when empty() is true, the behaviour
* is the same than after creating the randomizer
* @return true iff all the values were returned
*/
inline bool empty() const { return m_values.empty(); }
/**
* Returns the number of values still to be returned
* @return amount of values to return
*/
inline unsigned int left() const { return m_values.size(); }
/**
* Resets the randomizer as it were just created
*/
void reset();
protected:
/**
* Creates the vector with available values
*/
void createValues();
protected:
/// Min of range of values
int m_min;
/// Max of range of values
int m_max;
/// Available values
std::vector<int> m_values;
};
}
#endif

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/*
* File: Timestamp.cpp
* Author: Dorian Galvez-Lopez
* Date: March 2009
* Description: timestamping functions
*
* Note: in windows, this class has a 1ms resolution
*
* License: see the LICENSE.txt file
*
*/
#include <cstdio>
#include <cstdlib>
#include <ctime>
#include <cmath>
#include <sstream>
#include <iomanip>
#ifdef _WIN32
#ifndef WIN32
#define WIN32
#endif
#endif
#ifdef WIN32
#include <sys/timeb.h>
#define sprintf sprintf_s
#else
#include <sys/time.h>
#endif
#include "Timestamp.h"
using namespace std;
using namespace DUtils;
Timestamp::Timestamp(Timestamp::tOptions option)
{
if(option & CURRENT_TIME)
setToCurrentTime();
else if(option & ZERO)
setTime(0.);
}
Timestamp::~Timestamp(void)
{
}
bool Timestamp::empty() const
{
return m_secs == 0 && m_usecs == 0;
}
void Timestamp::setToCurrentTime(){
#ifdef WIN32
struct __timeb32 timebuffer;
_ftime32_s( &timebuffer ); // C4996
// Note: _ftime is deprecated; consider using _ftime_s instead
m_secs = timebuffer.time;
m_usecs = timebuffer.millitm * 1000;
#else
struct timeval now;
gettimeofday(&now, NULL);
m_secs = now.tv_sec;
m_usecs = now.tv_usec;
#endif
}
void Timestamp::setTime(const string &stime){
string::size_type p = stime.find('.');
if(p == string::npos){
m_secs = atol(stime.c_str());
m_usecs = 0;
}else{
m_secs = atol(stime.substr(0, p).c_str());
string s_usecs = stime.substr(p+1, 6);
m_usecs = atol(stime.substr(p+1).c_str());
m_usecs *= (unsigned long)pow(10.0, double(6 - s_usecs.length()));
}
}
void Timestamp::setTime(double s)
{
m_secs = (unsigned long)s;
m_usecs = (s - (double)m_secs) * 1e6;
}
double Timestamp::getFloatTime() const {
return double(m_secs) + double(m_usecs)/1000000.0;
}
string Timestamp::getStringTime() const {
char buf[32];
sprintf(buf, "%.6lf", this->getFloatTime());
return string(buf);
}
double Timestamp::operator- (const Timestamp &t) const {
return this->getFloatTime() - t.getFloatTime();
}
Timestamp& Timestamp::operator+= (double s)
{
*this = *this + s;
return *this;
}
Timestamp& Timestamp::operator-= (double s)
{
*this = *this - s;
return *this;
}
Timestamp Timestamp::operator+ (double s) const
{
unsigned long secs = (long)floor(s);
unsigned long usecs = (long)((s - (double)secs) * 1e6);
return this->plus(secs, usecs);
}
Timestamp Timestamp::plus(unsigned long secs, unsigned long usecs) const
{
Timestamp t;
const unsigned long max = 1000000ul;
if(m_usecs + usecs >= max)
t.setTime(m_secs + secs + 1, m_usecs + usecs - max);
else
t.setTime(m_secs + secs, m_usecs + usecs);
return t;
}
Timestamp Timestamp::operator- (double s) const
{
unsigned long secs = (long)floor(s);
unsigned long usecs = (long)((s - (double)secs) * 1e6);
return this->minus(secs, usecs);
}
Timestamp Timestamp::minus(unsigned long secs, unsigned long usecs) const
{
Timestamp t;
const unsigned long max = 1000000ul;
if(m_usecs < usecs)
t.setTime(m_secs - secs - 1, max - (usecs - m_usecs));
else
t.setTime(m_secs - secs, m_usecs - usecs);
return t;
}
bool Timestamp::operator> (const Timestamp &t) const
{
if(m_secs > t.m_secs) return true;
else if(m_secs == t.m_secs) return m_usecs > t.m_usecs;
else return false;
}
bool Timestamp::operator>= (const Timestamp &t) const
{
if(m_secs > t.m_secs) return true;
else if(m_secs == t.m_secs) return m_usecs >= t.m_usecs;
else return false;
}
bool Timestamp::operator< (const Timestamp &t) const
{
if(m_secs < t.m_secs) return true;
else if(m_secs == t.m_secs) return m_usecs < t.m_usecs;
else return false;
}
bool Timestamp::operator<= (const Timestamp &t) const
{
if(m_secs < t.m_secs) return true;
else if(m_secs == t.m_secs) return m_usecs <= t.m_usecs;
else return false;
}
bool Timestamp::operator== (const Timestamp &t) const
{
return(m_secs == t.m_secs && m_usecs == t.m_usecs);
}
string Timestamp::Format(bool machine_friendly) const
{
struct tm tm_time;
time_t t = (time_t)getFloatTime();
#ifdef WIN32
localtime_s(&tm_time, &t);
#else
localtime_r(&t, &tm_time);
#endif
char buffer[128];
if(machine_friendly)
{
strftime(buffer, 128, "%Y%m%d_%H%M%S", &tm_time);
}
else
{
strftime(buffer, 128, "%c", &tm_time); // Thu Aug 23 14:55:02 2001
}
return string(buffer);
}
string Timestamp::Format(double s) {
int days = int(s / (24. * 3600.0));
s -= days * (24. * 3600.0);
int hours = int(s / 3600.0);
s -= hours * 3600;
int minutes = int(s / 60.0);
s -= minutes * 60;
int seconds = int(s);
int ms = int((s - seconds)*1e6);
stringstream ss;
ss.fill('0');
bool b;
if((b = (days > 0))) ss << days << "d ";
if((b = (b || hours > 0))) ss << setw(2) << hours << ":";
if((b = (b || minutes > 0))) ss << setw(2) << minutes << ":";
if(b) ss << setw(2);
ss << seconds;
if(!b) ss << "." << setw(6) << ms;
return ss.str();
}

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/*
* File: Timestamp.h
* Author: Dorian Galvez-Lopez
* Date: March 2009
* Description: timestamping functions
* License: see the LICENSE.txt file
*
*/
#ifndef __D_TIMESTAMP__
#define __D_TIMESTAMP__
#include <iostream>
using namespace std;
namespace DUtils {
/// Timestamp
class Timestamp
{
public:
/// Options to initiate a timestamp
enum tOptions
{
NONE = 0,
CURRENT_TIME = 0x1,
ZERO = 0x2
};
public:
/**
* Creates a timestamp
* @param option option to set the initial time stamp
*/
Timestamp(Timestamp::tOptions option = NONE);
/**
* Destructor
*/
virtual ~Timestamp(void);
/**
* Says if the timestamp is "empty": seconds and usecs are both 0, as
* when initiated with the ZERO flag
* @return true iif secs == usecs == 0
*/
bool empty() const;
/**
* Sets this instance to the current time
*/
void setToCurrentTime();
/**
* Sets the timestamp from seconds and microseconds
* @param secs: seconds
* @param usecs: microseconds
*/
inline void setTime(unsigned long secs, unsigned long usecs){
m_secs = secs;
m_usecs = usecs;
}
/**
* Returns the timestamp in seconds and microseconds
* @param secs seconds
* @param usecs microseconds
*/
inline void getTime(unsigned long &secs, unsigned long &usecs) const
{
secs = m_secs;
usecs = m_usecs;
}
/**
* Sets the timestamp from a string with the time in seconds
* @param stime: string such as "1235603336.036609"
*/
void setTime(const string &stime);
/**
* Sets the timestamp from a number of seconds from the epoch
* @param s seconds from the epoch
*/
void setTime(double s);
/**
* Returns this timestamp as the number of seconds in (long) float format
*/
double getFloatTime() const;
/**
* Returns this timestamp as the number of seconds in fixed length string format
*/
string getStringTime() const;
/**
* Returns the difference in seconds between this timestamp (greater) and t (smaller)
* If the order is swapped, a negative number is returned
* @param t: timestamp to subtract from this timestamp
* @return difference in seconds
*/
double operator- (const Timestamp &t) const;
/**
* Returns a copy of this timestamp + s seconds + us microseconds
* @param s seconds
* @param us microseconds
*/
Timestamp plus(unsigned long s, unsigned long us) const;
/**
* Returns a copy of this timestamp - s seconds - us microseconds
* @param s seconds
* @param us microseconds
*/
Timestamp minus(unsigned long s, unsigned long us) const;
/**
* Adds s seconds to this timestamp and returns a reference to itself
* @param s seconds
* @return reference to this timestamp
*/
Timestamp& operator+= (double s);
/**
* Substracts s seconds to this timestamp and returns a reference to itself
* @param s seconds
* @return reference to this timestamp
*/
Timestamp& operator-= (double s);
/**
* Returns a copy of this timestamp + s seconds
* @param s: seconds
*/
Timestamp operator+ (double s) const;
/**
* Returns a copy of this timestamp - s seconds
* @param s: seconds
*/
Timestamp operator- (double s) const;
/**
* Returns whether this timestamp is at the future of t
* @param t
*/
bool operator> (const Timestamp &t) const;
/**
* Returns whether this timestamp is at the future of (or is the same as) t
* @param t
*/
bool operator>= (const Timestamp &t) const;
/**
* Returns whether this timestamp and t represent the same instant
* @param t
*/
bool operator== (const Timestamp &t) const;
/**
* Returns whether this timestamp is at the past of t
* @param t
*/
bool operator< (const Timestamp &t) const;
/**
* Returns whether this timestamp is at the past of (or is the same as) t
* @param t
*/
bool operator<= (const Timestamp &t) const;
/**
* Returns the timestamp in a human-readable string
* @param machine_friendly if true, the returned string is formatted
* to yyyymmdd_hhmmss, without weekday or spaces
* @note This has not been tested under Windows
* @note The timestamp is truncated to seconds
*/
string Format(bool machine_friendly = false) const;
/**
* Returns a string version of the elapsed time in seconds, with the format
* xd hh:mm:ss, hh:mm:ss, mm:ss or s.us
* @param s: elapsed seconds (given by getFloatTime) to format
*/
static string Format(double s);
protected:
/// Seconds
unsigned long m_secs; // seconds
/// Microseconds
unsigned long m_usecs; // microseconds
};
}
#endif

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DBoW2: bag-of-words library for C++ with generic descriptors
Copyright (c) 2015 Dorian Galvez-Lopez <http://doriangalvez.com> (Universidad de Zaragoza)
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. Neither the name of copyright holders nor the names of its
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
''AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDERS OR CONTRIBUTORS
BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
If you use it in an academic work, please cite:
@ARTICLE{GalvezTRO12,
author={G\'alvez-L\'opez, Dorian and Tard\'os, J. D.},
journal={IEEE Transactions on Robotics},
title={Bags of Binary Words for Fast Place Recognition in Image Sequences},
year={2012},
month={October},
volume={28},
number={5},
pages={1188--1197},
doi={10.1109/TRO.2012.2197158},
ISSN={1552-3098}
}

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You should have received this DBoW2 version along with ORB-SLAM2 (https://github.com/raulmur/ORB_SLAM2).
See the original DBoW2 library at: https://github.com/dorian3d/DBoW2
All files included in this version are BSD, see LICENSE.txt
We also use Random.h, Random.cpp, Timestamp.pp and Timestamp.h from DLib/DUtils.
See the original DLib library at: https://github.com/dorian3d/DLib
All files included in this version are BSD, see LICENSE.txt

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# This is the CMakeCache file.
# For build in directory: /home/hong/Desktop/ORB-SLAM2/dynamic_orb_slam-obj_track/Thirdparty/DBoW2/build
# It was generated by CMake: /usr/bin/cmake
# You can edit this file to change values found and used by cmake.
# If you do not want to change any of the values, simply exit the editor.
# If you do want to change a value, simply edit, save, and exit the editor.
# The syntax for the file is as follows:
# KEY:TYPE=VALUE
# KEY is the name of a variable in the cache.
# TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!.
# VALUE is the current value for the KEY.
########################
# EXTERNAL cache entries
########################
//Path to a program.
CMAKE_AR:FILEPATH=/usr/bin/ar
//Choose the type of build, options are: None(CMAKE_CXX_FLAGS or
// CMAKE_C_FLAGS used) Debug Release RelWithDebInfo MinSizeRel.
CMAKE_BUILD_TYPE:STRING=Release
//Enable/Disable color output during build.
CMAKE_COLOR_MAKEFILE:BOOL=ON
//CXX compiler
CMAKE_CXX_COMPILER:FILEPATH=/usr/bin/c++
//A wrapper around 'ar' adding the appropriate '--plugin' option
// for the GCC compiler
CMAKE_CXX_COMPILER_AR:FILEPATH=/usr/bin/gcc-ar-7
//A wrapper around 'ranlib' adding the appropriate '--plugin' option
// for the GCC compiler
CMAKE_CXX_COMPILER_RANLIB:FILEPATH=/usr/bin/gcc-ranlib-7
//Flags used by the compiler during all build types.
CMAKE_CXX_FLAGS:STRING=
//Flags used by the compiler during debug builds.
CMAKE_CXX_FLAGS_DEBUG:STRING=-g
//Flags used by the compiler during release builds for minimum
// size.
CMAKE_CXX_FLAGS_MINSIZEREL:STRING=-Os -DNDEBUG
//Flags used by the compiler during release builds.
CMAKE_CXX_FLAGS_RELEASE:STRING=-O3 -DNDEBUG
//Flags used by the compiler during release builds with debug info.
CMAKE_CXX_FLAGS_RELWITHDEBINFO:STRING=-O2 -g -DNDEBUG
//C compiler
CMAKE_C_COMPILER:FILEPATH=/usr/bin/cc
//A wrapper around 'ar' adding the appropriate '--plugin' option
// for the GCC compiler
CMAKE_C_COMPILER_AR:FILEPATH=/usr/bin/gcc-ar-7
//A wrapper around 'ranlib' adding the appropriate '--plugin' option
// for the GCC compiler
CMAKE_C_COMPILER_RANLIB:FILEPATH=/usr/bin/gcc-ranlib-7
//Flags used by the compiler during all build types.
CMAKE_C_FLAGS:STRING=
//Flags used by the compiler during debug builds.
CMAKE_C_FLAGS_DEBUG:STRING=-g
//Flags used by the compiler during release builds for minimum
// size.
CMAKE_C_FLAGS_MINSIZEREL:STRING=-Os -DNDEBUG
//Flags used by the compiler during release builds.
CMAKE_C_FLAGS_RELEASE:STRING=-O3 -DNDEBUG
//Flags used by the compiler during release builds with debug info.
CMAKE_C_FLAGS_RELWITHDEBINFO:STRING=-O2 -g -DNDEBUG
//Flags used by the linker.
CMAKE_EXE_LINKER_FLAGS:STRING=
//Flags used by the linker during debug builds.
CMAKE_EXE_LINKER_FLAGS_DEBUG:STRING=
//Flags used by the linker during release minsize builds.
CMAKE_EXE_LINKER_FLAGS_MINSIZEREL:STRING=
//Flags used by the linker during release builds.
CMAKE_EXE_LINKER_FLAGS_RELEASE:STRING=
//Flags used by the linker during Release with Debug Info builds.
CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFO:STRING=
//Enable/Disable output of compile commands during generation.
CMAKE_EXPORT_COMPILE_COMMANDS:BOOL=OFF
//Install path prefix, prepended onto install directories.
CMAKE_INSTALL_PREFIX:PATH=/usr/local
//Path to a program.
CMAKE_LINKER:FILEPATH=/usr/bin/ld
//Path to a program.
CMAKE_MAKE_PROGRAM:FILEPATH=/usr/bin/make
//Flags used by the linker during the creation of modules.
CMAKE_MODULE_LINKER_FLAGS:STRING=
//Flags used by the linker during debug builds.
CMAKE_MODULE_LINKER_FLAGS_DEBUG:STRING=
//Flags used by the linker during release minsize builds.
CMAKE_MODULE_LINKER_FLAGS_MINSIZEREL:STRING=
//Flags used by the linker during release builds.
CMAKE_MODULE_LINKER_FLAGS_RELEASE:STRING=
//Flags used by the linker during Release with Debug Info builds.
CMAKE_MODULE_LINKER_FLAGS_RELWITHDEBINFO:STRING=
//Path to a program.
CMAKE_NM:FILEPATH=/usr/bin/nm
//Path to a program.
CMAKE_OBJCOPY:FILEPATH=/usr/bin/objcopy
//Path to a program.
CMAKE_OBJDUMP:FILEPATH=/usr/bin/objdump
//Value Computed by CMake
CMAKE_PROJECT_NAME:STATIC=DBoW2
//Path to a program.
CMAKE_RANLIB:FILEPATH=/usr/bin/ranlib
//Flags used by the linker during the creation of dll's.
CMAKE_SHARED_LINKER_FLAGS:STRING=
//Flags used by the linker during debug builds.
CMAKE_SHARED_LINKER_FLAGS_DEBUG:STRING=
//Flags used by the linker during release minsize builds.
CMAKE_SHARED_LINKER_FLAGS_MINSIZEREL:STRING=
//Flags used by the linker during release builds.
CMAKE_SHARED_LINKER_FLAGS_RELEASE:STRING=
//Flags used by the linker during Release with Debug Info builds.
CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFO:STRING=
//If set, runtime paths are not added when installing shared libraries,
// but are added when building.
CMAKE_SKIP_INSTALL_RPATH:BOOL=NO
//If set, runtime paths are not added when using shared libraries.
CMAKE_SKIP_RPATH:BOOL=NO
//Flags used by the linker during the creation of static libraries.
CMAKE_STATIC_LINKER_FLAGS:STRING=
//Flags used by the linker during debug builds.
CMAKE_STATIC_LINKER_FLAGS_DEBUG:STRING=
//Flags used by the linker during release minsize builds.
CMAKE_STATIC_LINKER_FLAGS_MINSIZEREL:STRING=
//Flags used by the linker during release builds.
CMAKE_STATIC_LINKER_FLAGS_RELEASE:STRING=
//Flags used by the linker during Release with Debug Info builds.
CMAKE_STATIC_LINKER_FLAGS_RELWITHDEBINFO:STRING=
//Path to a program.
CMAKE_STRIP:FILEPATH=/usr/bin/strip
//If this value is on, makefiles will be generated without the
// .SILENT directive, and all commands will be echoed to the console
// during the make. This is useful for debugging only. With Visual
// Studio IDE projects all commands are done without /nologo.
CMAKE_VERBOSE_MAKEFILE:BOOL=FALSE
//Value Computed by CMake
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