LiDAR sensors play an important role in the perception stack of modern autonomous driving systems. Adverse weather conditions such as rain, fog and dust, as well as some (occasional) LiDAR hardware fault may cause the LiDAR to produce pointcloud with abnormal patterns such as scattered noise points and uncommon intensity values. In this paper, we propose a novel approach to detect whether a LiDAR is generating anomalous pointcloud by analyzing the pointcloud characteristics. Specifically, we develop a pointcloud quality metric based on the LiDAR points' spatial and intensity distribution to characterize the noise level of the pointcloud, which relies on pure mathematical analysis and does not require any labeling or training as learning-based methods do. Therefore, the method is scalable and can be quickly deployed either online to improve the autonomy safety by monitoring anomalies in the LiDAR data or offline to perform in-depth study of the LiDAR behavior over large amount of data. The proposed approach is studied with extensive real public road data collected by LiDARs with different scanning mechanisms and laser spectrums, and is proven to be able to effectively handle various known and unknown sources of pointcloud anomaly.

翻译：激光雷达传感器在现代自动驾驶系统的感知模块中扮演着关键角色。雨、雾、灰尘等恶劣天气条件，以及某些（偶发的）激光雷达硬件故障，可能导致激光雷达产生带有异常模式的点云，例如散乱噪声点和不常见的强度值。本文提出了一种新方法，通过分析点云特性来检测激光雷达是否生成了异常点云。具体而言，我们基于激光雷达点的空间和强度分布开发了一种点云质量度量标准，用以表征点云的噪声水平。该方法依赖于纯数学分析，且无需像基于学习的方法那样进行标注或训练。因此，该方法具有可扩展性，可快速部署：既可在线用于通过监测激光雷达数据中的异常来提升自主安全性，也可离线用于基于大量数据对激光雷达行为进行深入研究。利用具有不同扫描机制和激光光谱的激光雷达采集的大量真实公共道路数据，对所提方法进行了广泛研究，并证明其能够有效处理各种已知和未知来源的点云异常。