The LiDAR tracking library, for tracking objects obtaining from segmentation-based detection and improve segmentation.

1. We name your ros workspace as CATKIN_WS and git clone as a ROS package.

   # we recommand you to organize your workspace as following
   $ mkdir -p ~/tracking_ws/src
   
   # git clone basic common libraries
   $ cd ~/tracking_ws/src
   $ git clone https://github.com/zhz03/lidar_tracking.git
   # checkout ubuntu20 branch
   $ git checkout ubuntu20
   
   # build your ros workspace for our Tracking-help segmentation demo
   $ cd ~/tracking_ws
   $ catkin_make

2. Run demo with kitti rosbag:

   • Terminal 1: convert kitti into rosbag. Put your kitti2bag.py under your kitti dataset folder, like this:

     .
     ├── 2011_09_26
     │   ├── 2011_09_26_drive_0017_sync
     │   │   ├── image_00
     │   │   ├── image_01
     │   │   ├── image_02
     │   │   ├── image_03
     │   │   ├── oxts
     │   │   └── velodyne_points
     │   ├── calib_cam_to_cam.txt
     │   ├── calib_imu_to_velo.txt
     │   └── calib_velo_to_cam.txt
     └── kitti2bag.py
     
     

     In the terminal:

     # activate conda environment
     conda activate Your_environment
     python kitti2bag.py -t 2011_09_26 -r 0002 raw_synced .
     

     After you finish running the code, you will get:

     ├── kitti_2011_09_26_drive_0002_synced.bag
     

   • Play converted rosbag and modify the detection.yaml and tracking.yaml accordingly.

   • Terminal 2: launch Tracking-help Segmentation demo.

     $ cd ~/tracking_ws
     $ source devel/setup.bash
     $ roslaunch tracking_lib demo.launch

　detection.yaml and tracking.yaml configure the detection_node and tracking_node in sample. kitti/*.yaml configure the algorithm parameters for KiTTI Dataset, Segmenter.yaml and TrackingWorker.yaml separately for Seg-based Segmentation, Tracking.

./config
├── detection.yaml
├── kitti
│   ├── Segmenter.yaml
│   └── TrackingWorker.yaml
└── tracking.yaml