RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments
Zhiqiang Chen, Hongbo Chen, Yuhua Qi, Shipeng Zhong, Dapeng Feng, Jin Wu, Weisong Wen, Ming Liu
Abstract
LiDAR-based localization is valuable for applications like mining surveys and underground facility maintenance. However, existing methods can struggle when dealing with uninformative geometric structures in challenging scenarios. This paper presents RELEAD, a LiDAR-centric solution designed to address scan-matching degradation. Our method enables degeneracy-free point cloud registration by solving constrained ESIKF updates in the front end and incorporates multisensor constraints, even when dealing with outlier measurements, through graph optimization based on Graduated Non-Convexity (GNC). Additionally, we propose a robust Incremental Fixed Lag Smoother (rIFL) for efficient GNC-based optimization. RELEAD has undergone extensive evaluation in degenerate scenarios and has outperformed existing state-of-the-art LiDAR-Inertial odometry and LiDAR-Visual-Inertial odometry methods.
BibTeX
@inproceedings{icra2024_releadresilientl,
title = {RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments},
author = {Zhiqiang Chen and Hongbo Chen and Yuhua Qi and Shipeng Zhong and Dapeng Feng and Jin Wu and Weisong Wen and Ming Liu},
booktitle = {ICRA 2024},
year = {2024}
}