首次系统评估激光雷达里程计在真实污染下的鲁棒性，发现误差可超80%。
Evaluating and Improving the Robustness of LiDAR Odometry and Localization Under Real-World Corruptions
- 设计轻量级检测过滤流水线，识别污染类型并针对性修复点云。
- 修复后里程计误差恢复至接近干净数据水平（<0.5%）。
- 对学习型系统用污染数据微调，显著提升抗干扰能力。
激光雷达里程计与定位是机器人及自动驾驶中的核心应用。尽管先进系统在干净点云上表现优异，其对损坏数据的鲁棒性仍缺乏研究。本文构建首个涵盖18种真实合成污染的全面基准，评估激光雷达位姿估计技术的鲁棒性。结果显示，在污染条件下，里程计位置误差从0.5%飙升至超过80%，而定位性能保持稳定。为应对这一敏感性，提出两种互补策略：其一，设计轻量级检测-过滤流水线，分类点云污染类型并应用对应滤波器（如双边滤波去噪），分类准确率高，滤波后里程计精度恢复至接近清洁数据水平；其二，针对学习型系统，使用污染数据微调可显著提升所有测试污染下的鲁棒性，甚至在一条数据序列上提升了干净数据表现。
原文摘要 · Abstract (English)
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.
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