  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.

Robotics;Computer Vision and Pattern Recognition;Signal Processing