RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments

LiDAR-based localization is valuable for applica- tions like mining surveys and underground facility mainte- nance. However, existing methods can struggle when dealing with uninformative geometric structures in challenging scenar- ios. 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 mea- surements, through graph optimization based on Graduated Non-Convexity (GNC). Additionally, we propose a robust In- cremental 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.