Multiple objects detection, tracking and classification from LIDAR scans/point-clouds

PCL based ROS package to Detect/Cluster --> Track --> Classify static and dynamic objects in real-time from LIDAR scans implemented in C++.

Features:

• K-D tree based point cloud processing for object feature detection from point clouds
• Unsupervised k-means clustering based on detected features and refinement using RANSAC
• Stable tracking (object ID & data association) with an ensemble of Kalman Filters
• Robust compared to k-means clustering with mean-flow tracking

Usage:

Follow the steps below to use this (multi_object_tracking_lidar) package:

1. Create a catkin workspace (if you do not have one setup already).
2. Navigate to the src folder in your catkin workspace: cd ~/catkin_ws/src
3. Clone this repository: git clone https://github.com/praveen-palanisamy/multiple-object-tracking-lidar.git
4. Compile and build the package: cd ~/catkin_ws && catkin_make
5. Add the catkin workspace to your ROS environment: source ~/catkin_ws/devel/setup.bash
6. Run the kf_tracker ROS node in this package: rosrun multi_object_tracking_lidar kf_tracker

If all went well, the ROS node should be up and running! As long as you have the point clouds published on to the filtered_cloud rostopic, you should see outputs from this node published onto the obj_id, cluster_0, cluster_1, …, cluster_5 topics along with the markers on viz topic which you can visualize using RViz.

Supported point-cloud streams/sources:

The input point-clouds can be from:

1. A real LiDAR or
2. A simulated LiDAR or
3. A point cloud dataset or
4. Any other data source that produces point clouds