LiDAR odometry and localization are two widely used and fundamental applications in robotic and autonomous driving systems. Although state-of-the-art (SOTA) systems achieve high accuracy on clean point clouds, their robustness to corrupted data remains largely unexplored. We present the first comprehensive benchmark to evaluate the robustness of LiDAR pose-estimation techniques under 18 realistic synthetic corruptions. Our results show that, under these corruptions, odometry position errors escalate from 0.5% to more than 80%, while localization performance stays consistently high. To address this sensitivity, we propose two complementary strategies. First, we design a lightweight detection-and-filter pipeline that classifies the point cloud corruption and applies a corresponding filter (e.g., bilateral filter for noise) to restore the point cloud quality. Our classifier accurately identifies each corruption type, and the filter effectively restores odometry accuracy to near-clean data levels. Second, for learning-based systems, we show that fine-tuning using the corrupted data substantially improves robustness across all tested corruptions and even boosts performance on clean point clouds on one data sequence.

翻译：激光雷达里程计与定位是机器人与自动驾驶系统中两个广泛使用的基础应用。尽管现有最先进系统在洁净点云上实现了高精度，但其对受干扰数据的鲁棒性在很大程度上仍未得到充分探索。我们提出了首个综合性基准测试，用于评估激光雷达位姿估计技术在18种真实合成干扰下的鲁棒性。我们的结果表明，在这些干扰下，里程计的位置误差从0.5%激增至80%以上，而定位性能则始终保持较高水平。为应对这种敏感性，我们提出了两种互补策略。首先，我们设计了一个轻量级的检测-过滤流程，该流程对点云干扰进行分类并应用相应的滤波器（例如，针对噪声的双边滤波器）以恢复点云质量。我们的分类器能准确识别每种干扰类型，且滤波器能有效将里程计精度恢复至接近洁净数据的水平。其次，对于基于学习的系统，我们证明使用受干扰数据进行微调能显著提升其在所有测试干扰下的鲁棒性，甚至在一个数据序列上提升了其在洁净点云上的性能。