[humble-devel] Add tracker_with_cloud_node

[Alpaca-zip]

PR Type

• Feature
• Bug fix
• Refactor
• Documentation
• Other

Overview

• Implement tracker_with_cloud_node which provides functionality for 3D object detection by integrating 2D detections, mask image, LiDAR data, and camera information.
• Modified to use ament_cmake_python as build tool.
• Duplicate with [noetic-devel] Add voxel grid filter and support for processing points with masks #35 .

Usage

tracker_node & tracker_with_cloud_node

$ ros2 launch ultralytics_ros tracker_with_cloud.launch.xml debug:=true

tracker_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)

Test

• I have tested this change with KITTI data set.
• All GitHub Actions passed.