This paper presents a safe, efficient, and agile ground vehicle navigation algorithm for 3D off-road terrain environments. Off-road navigation is subject to uncertain vehicle-terrain interactions caused by different terrain conditions on top of 3D terrain topology. The existing works are limited to adopt overly simplified vehicle-terrain models. The proposed algorithm learns the terrain-induced uncertainties from driving data and encodes the learned uncertainty distribution into the traversability cost for path evaluation. The navigation path is then designed to optimize the uncertainty-aware traversability cost, resulting in a safe and agile vehicle maneuver. Assuring real-time execution, the algorithm is further implemented within parallel computation architecture running on Graphics Processing Units (GPU).
@article{arxiv.2209.09177,
title = {Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains},
author = {Hojin Lee and Junsung Kwon and Cheolhyeon Kwon},
journal= {arXiv preprint arXiv:2209.09177},
year = {2022}
}
Comments
6 pages, 6 figures, submitted to International Conference on Robotics and Automation (ICRA 2023)