  When using LiDAR semantic segmentation models for safety-critical
applications such as autonomous driving, it is essential to understand and
improve their robustness with respect to a large range of LiDAR corruptions. In
this paper, we aim to comprehensively analyze the robustness of LiDAR semantic
segmentation models under various corruptions. To rigorously evaluate the
robustness and generalizability of current approaches, we propose a new
benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR
corruptions in three groups, namely adverse weather, measurement noise and
cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic
segmentation models, especially spanning different input representations (e.g.,
point clouds, voxels, projected images, and etc.), network architectures and
training schemes. Through this study, we obtain two insights: 1) We find out
that the input representation plays a crucial role in robustness. Specifically,
under specific corruptions, different representations perform variously. 2)
Although state-of-the-art methods on LiDAR semantic segmentation achieve
promising results on clean data, they are less robust when dealing with noisy
data. Finally, based on the above observations, we design a robust LiDAR
segmentation model (RLSeg) which greatly boosts the robustness with simple but
effective modifications. It is promising that our benchmark, comprehensive
analysis, and observations can boost future research in robust LiDAR semantic
segmentation for safety-critical applications.
