Evaluating and Improving the Robustness of LiDAR-based Localization and Mapping

Bo Yang, Tri Minh Triet Pham, Jinqiu Yang

Abstract: Accurate and reliable pose estimation, i.e., determining the precise position and orientation of autonomous robots and vehicles, is critical for tasks like navigation and mapping. LiDAR is a widely used sensor for pose estimation, with odometry and localization being two primary tasks. LiDAR odometry estimates the relative motion between consecutive scans, while LiDAR localization aligns real-time scans with a pre-recorded map to obtain a global pose. Although they have different objectives and application scenarios, both rely on point cloud registration as the underlying technique and face shared challenges of data corruption caused by adverse conditions (e.g., rain). While state-of-the-art (SOTA) pose estimation systems achieved high accuracy on clean data, their robustness to corrupted data remains unclear. In this work, we propose a framework to systematically evaluate five SOTA LiDAR pose estimation systems across 18 synthetic real-world point cloud corruptions. Our experiments reveal that odometry systems degrade significantly under specific corruptions, with relative position errors increasing from 0.5% to more than 80%, while localization systems remain highly robust. We further demonstrate that denoising techniques can effectively mitigate the adverse effects of noise-induced corruptions, and re-training learning-based systems with corrupted data significantly enhances the robustness against various corruption types.