Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds

Abstract

LiDAR sensors have the advantage of being able to generate high-resolution imaging quickly during both day and night; however, their performance is severely limited in adverse weather conditions such as snow, rain, and dense fog. Consequently, many researchers are actively working to overcome these limitations by applying sensor fusion with radar and optical cameras to LiDAR. While studies on the denoising of point clouds acquired by LiDAR in adverse weather have been conducted recently, the results are still insufficient for application to autonomous vehicles because of speed and accuracy performance limitations. Therefore, we propose a new intensity-based filter that differs from the existing distance-based filter, which limits the speed. The proposed method showed overwhelming performance advantages in terms of both speed and accuracy by removing only snow particles while leaving important environmental features. The intensity criteria for snow removal were derived based on an analysis of the properties of laser light and snow particles.