Computer Science > Robotics
arXiv:2402.18934 (cs)
[Submitted on 29 Feb 2024 (v1), last revised 15 Mar 2024 (this version, v2)]
Title:RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments
View a PDF of the paper titled RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments, by Zhiqiang Chen and 7 other authors
View PDFHTML (experimental)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.
| Subjects: | Robotics (cs.RO) |
| Cite as: | arXiv:2402.18934 [cs.RO] |
| (orarXiv:2402.18934v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2402.18934 arXiv-issued DOI via DataCite | |
| Journal reference: | published in ICRA 2024 |
Submission history
From: Zhiqiang Chen [view email][v1] Thu, 29 Feb 2024 08:01:47 UTC (15,181 KB)
[v2] Fri, 15 Mar 2024 10:55:39 UTC (15,181 KB)
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View a PDF of the paper titled RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments, by Zhiqiang Chen and 7 other authors
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