A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter

Abstract

Accurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive impression of their environment. Unfortunately, the ambient noise caused by varying weather conditions immediately affects the ability of autonomous vehicles to accurately understand their environment and its expected impact. In recent years, researchers have improved the environmental perception capabilities of simultaneous localization and mapping (SLAM), object detection and tracking, semantic segmentation and panoptic segmentation, but relatively few studies have focused on enhancing environmental perception capabilities in adverse weather conditions, such as rain, snow and fog. To enhance the environmental perception of autonomous vehicles in adverse weather, we developed a dynamic filtering method called Dynamic Distance–Intensity Outlier Removal (DDIOR), which integrates the distance and intensity of points based on the systematic and accurate analysis of LiDAR point cloud data characteristics in snowy weather. Experiments on the publicly available WADS dataset (Winter Adverse Driving dataSet) showed that our method can efficiently remove snow noise while fully preserving the detailed features of the environment.