25 lines
1.8 KiB
Markdown
25 lines
1.8 KiB
Markdown
# Multiple objects detection, tracking and classification from LIDAR scans/point-clouds
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PCL based ROS package to Detect/Cluster --> Track --> Classify static and dynamic objects in real-time from LIDAR scans implemented in C++.
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### Features:
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- K-D tree based point cloud processing for object feature detection from point clouds
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- Unsupervised k-means clustering based on detected features and refinement using RANSAC
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- Stable tracking (object ID & data association) with an ensemble of Kalman Filters
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- Robust compared to k-means clustering with mean-flow tracking
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### Usage:
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Follow the steps below to use this (`multi_object_tracking_lidar`) package:
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1. [Create a catkin workspace](http://wiki.ros.org/catkin/Tutorials/create_a_workspace) (if you do not have one setup already).
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1. Navigate to the `src` folder in your catkin workspace: `cd ~/catkin_ws/src`
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1. Clone this repository: `git clone https://github.com/praveen-palanisamy/multiple-object-tracking-lidar.git`
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1. Compile and build the package: `cd ~/catkin_ws && catkin_make`
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1. Add the catkin workspace to your ROS environment: `source ~/catkin_ws/devel/setup.bash`
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1. Run the `kf_tracker` ROS node in this package: `rosrun multi_object_tracking_lidar kf_tracker`
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If all went well, the ROS node should be up and running! As long as you have the point clouds (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) 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.
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