Dynamic obstacle avoidance is one crucial component for compliant navigation in crowded environments. In this paper we present a system for accurate and reliable detection and tracking of dynamic objects using noisy point cloud data generated by stereo cameras. Our solution is real-time capable and specifically designed for the deployment on computationally-constrained unmanned ground vehicles. The proposed approach identifies individual objects in the robot's surroundings and classifies them as either static or dynamic. The dynamic objects are labeled as either a person or a generic dynamic object. We then estimate their velocities to generate a 2D occupancy grid that is suitable for performing obstacle avoidance. We evaluate the system in indoor and outdoor scenarios and achieve real-time performance on a consumer-grade computer. On our test-dataset, we reach a MOTP of 0.07±0.07m, and a MOTA of 85.3% for the detection and tracking of dynamic objects. We reach a precision of 96.9% for the detection of static objects.
@article{arxiv.2007.10743,
title = {Leveraging Stereo-Camera Data for Real-Time Dynamic Obstacle Detection and Tracking},
author = {Thomas Eppenberger and Gianluca Cesari and Marcin Dymczyk and Roland Siegwart and Renaud Dubé},
journal= {arXiv preprint arXiv:2007.10743},
year = {2020}
}