Enhancing Robustness of LiDAR-Based Perception in Adverse Weather using Point Cloud Augmentations

Abstract

LiDAR-based perception systems have become widely adopted in autonomous vehicles. However, their performance can be severely degraded in adverse weather conditions, such as rain, snow or fog. To address this challenge, we propose a method for improving the robustness of LiDAR-based perception in adverse weather, using data augmentation techniques on point clouds. We use novel as well as established data augmentation techniques, such as realistic weather simulations, to provide a wide variety of training data for LiDAR-based object detectors. The performance of the state-of-the-art detector Voxel R-CNN using the proposed augmentation techniques is evaluated on a data set of real-world point clouds collected in adverse weather conditions. The achieved improvements in average precision (AP) are 4.00 p.p. in fog, 3.35 p.p. in snow, and 4.87 p.p. in rain at moderate difficulty. Our results suggest that data augmentations on point clouds are an effective way to improve the robustness of LiDAR-based object detection in adverse weather.