  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.
