RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments

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.

Figures (7)

• Figure 1: Comparison of methods for metro tunnel reconstruction and associated trajectories (red lines). In this degenerate scenario, other methods fail due to the lack of distinctive features, resulting in a slipping trajectory, while RELEAD successfully reconstructs the scene. In the bottom row: the left panel illustrates degradation detection within the tunnel, with arrows indicating degradation direction. The right panel displays estimated localizability categories for each direction.
• Figure 2: System overview of RELEAD consists of three parts: process block, constrained ESIKF update process and graph-based sensor integration module.
• Figure 3: Degeneration-Aware LiDAR Measurement Update.
• Figure 4: Trajectory results for three sequences in S3E dataset.
• Figure 5: Mapping and trajectory results of experiment in urban road tunnel.