De-noising of Lidar Point Clouds Corrupted by Snowfall

Abstract

A common problem in autonomous driving is designing a system that can operate in adverse weather conditions. Falling rain and snow tends to corrupt sensor measurements, particularly for lidar sensors. Surprisingly, very little research has been published on methods to de-noise point clouds which are collected by lidar in rainy or snowy weather conditions. In this paper, we present a method for removing snow noise by processing point clouds using a 3D outlier detection algorithm. Our method, the dynamic radius outlier removal filter, accounts for the variation in point cloud density with increasing distance from the sensor, with the goal of removing the noise caused by snow while retaining detail in environmental features (which is necessary for autonomous localization and navigation). The proposed method outperforms other noise-removal methods, including methods which operate on depth image representations of the lidar scans. We show on point clouds obtained while driving in falling snow that we can simultaneously obtain > 90% precision and recall, indicating that the proposed method is effective at removing snow, without removing environmental features.