Depth-prior-based LiDAR point cloud de-noising method leveraging range-gated imaging

Abstract

Light Detection and Ranging (LiDAR) has been widely adopted in modern self-driving vehicles and mobile robotics, providing 3D information of the scene and surrounding objects. However, LiDAR systems suffer from many kinds of noise, and its noisy point clouds degrade downstream tasks. Existing LiDAR point cloud de-noising methods are time-consuming or cannot deal with the noise caused by occlusions or penetrating transparent surfaces. In this paper, we introduce a depth-prior-based LiDAR point clouds de-noising method to deal with all types of noise in LiDAR point clouds in real time. The depth prior is derived from the fundamental principles of range-gated imaging and divides the depth of field into three parts, which can provide an effective depth signal. The LiDAR point cloud, which is acquired in synchronization with gated images, is projected into a depth map, and points whose depth is inconsistent with the depth prior can be regarded as noise and can be removed, finally. We conduct an ablation study and compare the proposed method with existing de-noising methods using the Gated2Depth dataset, which is to our knowledge the first long-range-gated dataset specifically designed for 3D detection in adverse weather conditions and includes all the necessary data. The results demonstrated that the proposed method achieves superior performance across all metrics.