LIDSOR: A FILTER FOR REMOVING RAIN AND SNOW NOISE POINTS FROM LIDAR POINT CLOUDS IN RAINY AND SNOWY WEATHER
Hui Huang, Xinyuan Yan, Jin Min Yang, Yue Cao, Xiang Zhang
Abstract
Abstract. As autonomous driving technology advances, ensuring the system's safety in rain and snow has emerged as a pivotal research topic. In rainy and snowy weather, rain and snow can generate noise points within the point cloud captured by the Light Detection and Ranging (LiDAR), significantly impeding the LiDAR's sensing capability. To address this problem, we first manually label the point cloud data gathered in rain and snow, categorizing all points into noise points and non-noise points. Subsequently, we analyze the intensity and spatial distribution characteristics of the rain and snow noise points and employ the gamma distribution curve to illustrate the spatial distribution characteristics of these noise points. Finally, we propose a Low-Intensity Dynamic Statistical Outlier Removal (LIDSOR) filter, an enhancement of the existing Dynamic Statistical Outlier Removal (DSOR) filter. Experimental results suggest that the LIDSOR filter can effectively eliminate rain and snow noise points while preserving more environmental feature points. Additionally, it consumes fewer computational resources. The filter we propose in this paper significantly contributes to the safe operation of the autonomous driving system in diverse complex environments.