# lidar-object-tracking

**Repository Path**: todd0410/lidar-object-tracking

## Basic Information

- **Project Name**: lidar-object-tracking
- **Description**: a ros package using clustering and Kalman filter to achive object detection and tracking
- **Primary Language**: C++
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2022-09-17
- **Last Updated**: 2022-09-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# lidar-object-tracking

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 (`lidar-object-tracking`) 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
1. Clone this repository
1. Compile and build the package: `catkin_make`
1. Add the catkin workspace to your ROS environment: `source ~/catkin_ws/devel/setup.bash`
1. Run the `lidar-object-tracking.launch` launch file in this package: `roslaunch multi_object_tracking_lidar lidar-object-tracking.launch`

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