Leveraging Stereo-Camera Data for Real-Time Dynamic Obstacle Detection and Tracking
Thomas Eppenberger, Gianluca Cesari, Marcin Dymczyk, Roland Siegwart, Renaud Dubé
Abstract
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 consumergrade 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.
BibTeX
@inproceedings{iros2020_leveragingstereo,
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},
booktitle = {IROS 2020},
year = {2020}
}