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.

翻译：基于激光雷达的定位在矿山测量、地下设施维护等应用中具有重要价值。然而，现有方法在应对具有非信息性几何结构的挑战性场景时仍存在不足。本文提出RELEAD——一种专为解决扫描配准退化问题设计的激光雷达中心化方案。该方法通过在前端求解约束型ESIKF更新实现无退化点云配准，并基于渐进非凸性（GNC）的图优化技术，在存在异常测量值时仍能融合多传感器约束。此外，我们提出鲁棒增量式固定滞后平滑器（rIFL）以实现基于GNC的高效优化。RELEAD已在退化场景中经过全面评估，其性能优于现有最先进的激光雷达-惯性里程计与激光雷达-视觉-惯性里程计方法。