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C++ implementation to Detect, track and classify multiple objects using LIDAR scans or point cloud
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# Multiple objects detection, tracking and classification from LIDAR scans/point-clouds
[](https://zenodo.org/badge/latestdoi/47581608)
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 euclidean cluster extraction (3D) or k-means clustering based on detected features and refinement using RANSAC (2D)
- 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](http://wiki.ros.org/catkin/Tutorials/create_a_workspace) (if you do not have one setup already).
1. Navigate to the `src` folder in your catkin workspace: `cd ~/catkin_ws/src`
1. Clone this repository: `git clone https://github.com/praveen-palanisamy/multiple-object-tracking-lidar.git`
1. Compile and build the package: `cd ~/catkin_ws && catkin_make`
1. Add the catkin workspace to your ROS environment: `source ~/catkin_ws/devel/setup.bash`
1. 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
**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.
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.
## Citing
If you use the code or snippets from this repository in your work, please cite:
```bibtex
@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](https://github.com/praveen-palanisamy/multiple-object-tracking-lidar/wiki/velodyne_vlp16)
Excerpt of 3,402 characters
Read on GitHub
Praveen Palanisamy · @microsoft
36
Mohamed Zahran
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:becbd7194d72510b, topic:object-tracking, name:object tracking, readme:object tracking
matched fp:becbd7194d72510b, topic:ros