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

Light Detection And Ranging (LiDAR) has been widely adopted to modern self-driving vehicles and mobile robotics, providing 3D information of the scene and surrounding objects. However, LiDAR suffer from many kinds of noises 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 noises 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 effective depth signal. LiDAR point cloud is projected into a depth map and points whose depth is inconsistent with the depth prior can be regarded as noises and removed finally. In experiments, the proposed method is compared with existing de-noising methods and achieve superior performance. In addition, we also demonstrate how denoised LiDAR data influence the accuracy of vision-guided range-gated 3D imaging.