RP Tracking is an easy-to-use system for tracking mobile robots and drones in indoor lab environments. It uses time-of-flight based sensors, like LiDAR, to detect moving objects in point clouds. Contrary to off-the-shelf systems, this work can be used with any time-of-flight sensor that captures accurate data and can be integrated into the ROS 1 environment. Furthermore, it supports multiple sensors to increase accuracy and area size.

Example

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Usage

1. Clone this repository into your catkin workspace.

2. Install missing ROS packages (probably only jsk_recognition_msgs).

3. Build the software using catkin_make.

4. Source the current worksapce using source devel/setup.bash'.

5. Execute the following command. Replace MODEL_PATH with a path to a model file (e.g. "CATKIN_WS/src/rp_tracking/rp_tracking/rp_tracking/models/tello_cage.model", replace CATKIN_WS with the path to your workspace) and set TOPIC to the name of the topic, where the input point cloud is published at.

   roslaunch rp_tracking rp_tracking.launch model:=MODEL_PATH input_cloud:=/TOPIC

For more detailed information on how to use RP Tracking, please take a look at the documentation. To test this software, a demo is made available.

Citation

Please cite the following paper in your publications if you use Deploy-to-Grading in your research:

@inproceedings{RP_Tracking2025,
    author    = "Kirsch, André and Rexilius, Jan",
    title     = "An Easy-to-Use System for Tracking Robotic Platforms using Time-of-Flight Sensors in Lab Environments",
    year      = 2025,
    booktitle = "14th International Conference on Pattern Recognition Applications and Methods (ICPRAM)",
    doi       = "10.5220/0013110500003905"
}

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License

This work by André Kirsch and Jan Rexilius is licensed under MIT.