multi_object_tracking_lidar repository

Repository Summary

Checkout URI https://github.com/praveen-palanisamy/multiple-object-tracking-lidar.git
VCS Type git
VCS Version master
Last Updated 2019-09-07
Dev Status MAINTAINED
Released RELEASED

Packages

Name Version
multi_object_tracking_lidar 1.0.1

README

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

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

Wiki

Checkout the Wiki pages

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

Repository Summary

Checkout URI https://github.com/praveen-palanisamy/multiple-object-tracking-lidar.git
VCS Type git
VCS Version master
Last Updated 2019-09-07
Dev Status MAINTAINED
Released RELEASED

Packages

Name Version
multi_object_tracking_lidar 1.0.1

README

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

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

Wiki

Checkout the Wiki pages

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

Repository Summary

Checkout URI https://github.com/praveen-palanisamy/multiple-object-tracking-lidar.git
VCS Type git
VCS Version master
Last Updated 2019-09-07
Dev Status MAINTAINED
Released RELEASED

Packages

Name Version
multi_object_tracking_lidar 1.0.1

README

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

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

Wiki

Checkout the Wiki pages

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

Repository Summary

Checkout URI https://github.com/praveen-palanisamy/multiple-object-tracking-lidar.git
VCS Type git
VCS Version master
Last Updated 2019-09-07
Dev Status MAINTAINED
Released RELEASED

Packages

Name Version
multi_object_tracking_lidar 1.0.1

README

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

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

Wiki

Checkout the Wiki pages

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