SEB-Naver: A SE(2)-based Local Navigation Framework for Car-like Robots on Uneven Terrain
Xiaoying Li, Long Xu, Xiaolin Huang, Donglai Xue, Zhihao Zhang, Zhichao Han, Chao Xu, Yanjun Cao
Abstract
Autonomous navigation of car-like robots on uneven terrain poses unique challenges compared to flat terrain, particularly in traversability assessment and terrain-associated kinematic modelling for motion planning. This paper introduces SEB-Naver, a novel SE(2)-based local navigation framework designed to overcome these challenges. First, we propose an efficient traversability assessment method for SE(2) grids, leveraging GPU parallel computing to enable real-time updates and maintenance of local maps. Second, inspired by differential flatness, we present an optimization-based trajectory planning method that integrates terrain-associated kinematic models, significantly improving both planning efficiency and trajectory quality. Finally, we unify these components into SEB-Naver, achieving real-time terrain assessment and trajectory optimization. Extensive simulations and real-world experiments demonstrate the effectiveness and efficiency of our approach. The code is at https://github.com/ZJU-FAST-Lab/seb_naver.
BibTeX
@inproceedings{iros2025_sebnaverase2base,
title = {SEB-Naver: A SE(2)-based Local Navigation Framework for Car-like Robots on Uneven Terrain},
author = {Xiaoying Li and Long Xu and Xiaolin Huang and Donglai Xue and Zhihao Zhang and Zhichao Han and Chao Xu and Yanjun Cao and Fei Gao},
booktitle = {IROS 2025},
year = {2025}
}