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Package Summary

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

Package Summary

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

Package Summary

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

Package Summary

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

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

DOI

Sample demo of multiple object tracking using LIDAR scans

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

Citing

If you use the code or snippets from this repository in your work, please cite:

@software{praveen_palanisamy_2019_3559187,
  author       = {Praveen Palanisamy},
  title        = {{praveen-palanisamy/multiple-object-tracking-lidar:
                   Multiple-Object-Tracking-from-Point-Clouds_v1.0.2}},
  month        = dec,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {1.0.2},
  doi          = {10.5281/zenodo.3559187},
  url          = {https://doi.org/10.5281/zenodo.3559186}
}

Wiki

Checkout the Wiki pages (https://github.com/praveen-palanisamy/multiple-object-tracking-lidar/wiki)

1. Multiple-object tracking from pointclouds using a Velodyne VLP-16


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Wiki: multi_object_tracking_lidar (last edited 2020-01-21 01:43:01 by Praveen-Palanisamy)