GeoYolo-SLAM/ultralytics_ros/README.md

ultralytics_ros

ROS package for real-time object detection and segmentation using the Ultralytics YOLO, enabling flexible integration with various robotics applications.

predict_node	predict_with_cloud_node

• The predict_node provides real-time object detection and segmentation on incoming ROS image messages using the Ultralytics YOLO model.
• The predict_with_cloud_node provides functionality for 3D object detection by integrating 2D detections, mask image, LiDAR data, and camera information.

Setup ⚙

$ cd ~/catkin_ws/src
$ git clone -b melodic-devel https://github.com/Alpaca-zip/ultralytics_ros.git
$ rosdep install -r -y -i --from-paths .
$ pip install pipenv
$ cd ultralytics_ros
$ pipenv install
$ pipenv shell
$ cd ~/catkin_ws
$ catkin build

Run 🚀

predict_node

$ roslaunch ultralytics_ros predict.launch debug:=true

predict_node & predict_with_cloud_node

$ roslaunch ultralytics_ros predict_with_cloud.launch debug:=true

NOTE: If the 3D bounding box is not displayed correctly, please consider using a lighter yolo model(yolov8n.pt) or increasing the voxel_leaf_size.

predict_node

Params

• yolo_model: Pre-trained Weights.
  For yolov8, you can choose yolov8*.pt, yolov8*-seg.pt.

  YOLOv8
  YOLOv8-seg

  See also: https://docs.ultralytics.com/models/

• input_topic: Topic name for input image.

• result_topic: Topic name of the custom message containing the 2D bounding box and the mask image.

• result_image_topic: Topic name of the image on which the detection and segmentation results are plotted.

• conf_thres: Confidence threshold below which boxes will be filtered out.

• iou_thres: IoU threshold below which boxes will be filtered out during NMS.

• max_det: Maximum number of boxes to keep after NMS.

• device: Device to run the model on(e.g. cpu or cuda:0).

  <arg name="device" default="cpu"/> <!-- cpu -->
  

  <arg name="device" default="0"/> <!-- cuda:0 -->
  

• classes: List of class indices to consider.

  <arg name="classes" default="[0, 1]"/> <!-- person, bicycle -->
  

  See also: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco128.yaml

• result_conf: Whether to plot the detection confidence score.

• result_line_width: Line width of the bounding boxes.

• result_font_size: Font size of the text.

• result_labels: Font to use for the text.

• result_font: Whether to plot the label of bounding boxes.

• result_boxes: Whether to plot the bounding boxes.

Topics

• Subscribed Topics:
  • Image data from input_topic parameter. (sensor_msgs/Image)
• Published Topics:
  • Plotted images to result_image_topic parameter. (sensor_msgs/Image)
  • Detected objects(2D bounding box, mask image) to result_topic parameter. (ultralytics_ros/YoloResult)

    std_msgs/Header header
    vision_msgs/Detection2DArray detections
    sensor_msgs/Image[] masks
    

predict_with_cloud_node

Params

• camera_info_topic: Topic name for camera info.
• lidar_topic: Topic name for lidar.
• yolo_result_topic: Topic name of the custom message containing the 2D bounding box and the mask image.
• yolo_3d_result_topic: Topic name for 3D bounding box.
• cluster_tolerance: Spatial cluster tolerance as a measure in the L2 Euclidean space.
• voxel_leaf_size: Voxel size for pointcloud downsampling.
• min_cluster_size: Minimum number of points that a cluster needs to contain.
• max_cluster_size: Maximum number of points that a cluster needs to contain.

Topics

• Subscribed Topics:
  • Camera info from camera_info_topic parameter. (sensor_msgs/CameraInfo)
  • Lidar data from lidar_topic parameter. (sensor_msgs/PointCloud2)
  • Detected objects(2D bounding box, mask image) from yolo_result_topic parameter. (ultralytics_ros/YoloResult)

    std_msgs/Header header
    vision_msgs/Detection2DArray detections
    sensor_msgs/Image[] masks
    

• Published Topics:
  • Detected cloud points to /detection_cloud topic. (sensor_msgs/PointCloud2)
  • Detected objects(3D bounding box) to yolo_3d_result_topic parameter. (vision_msgs/Detection3DArray)
  • Visualization markers to /detection_marker topic. (visualization_msgs/MarkerArray)

Docker with KITTI datasets 🐳

Docker Pull & Run

$ docker pull alpacazip/ultralytics_ros:melodic
$ docker run -p 6080:80 --shm-size=512m alpacazip/ultralytics_ros:melodic

Run predict_node & predict_with_cloud_node

$ roscd ultralytics_ros && pipenv shell
$ roslaunch ultralytics_ros kitti_predict_with_cloud.launch
$ cd ~/. && rosbag play kitti_2011_09_26_drive_0106_synced.bag --clock --loop