63 lines
3.3 KiB
Markdown
63 lines
3.3 KiB
Markdown
# Multiple objects detection, tracking and classification from LIDAR scans/point-clouds
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[](https://zenodo.org/badge/latestdoi/47581608)
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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 euclidean cluster extraction (3D) or k-means clustering based on detected features and refinement using RANSAC (2D)
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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 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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### Supported point-cloud streams/sources:
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The input point-clouds can be from:
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1. A real LiDAR or
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2. A simulated LiDAR or
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3. A point cloud dataset or
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4. Any other data source that produces point clouds
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**Note:** This package expects valid point cloud data as input. The point clouds you publish to the "`filtered_cloud`" is **not** expected to contain NaNs. The point cloud filtering is somewhat task and application dependent and therefore it is not done by this module.
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PCL library provides `pcl::removeNaNFromPointCloud (...)` method to filter out NaN points. You can refer to [this example code snippet](https://github.com/praveen-palanisamy/multiple-object-tracking-lidar/issues/29#issuecomment-672098760) to easily filter out NaN points in your point cloud.
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## Citing
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If you use the code or snippets from this repository in your work, please cite:
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```bibtex
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@software{praveen_palanisamy_2019_3559187,
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author = {Praveen Palanisamy},
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title = {{praveen-palanisamy/multiple-object-tracking-lidar:
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Multiple-Object-Tracking-from-Point-Clouds_v1.0.2}},
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month = dec,
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year = 2019,
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publisher = {Zenodo},
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version = {1.0.2},
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doi = {10.5281/zenodo.3559187},
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url = {https://doi.org/10.5281/zenodo.3559186}
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}
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```
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### Wiki
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[Checkout the Wiki pages](https://github.com/praveen-palanisamy/multiple-object-tracking-lidar/wiki)
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1. [Multiple-object tracking from pointclouds using a Velodyne VLP-16](https://github.com/praveen-palanisamy/multiple-object-tracking-lidar/wiki/velodyne_vlp16)
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