A New Method of 3D Point Cloud Data Processing in Low-speed Self-driving Car

A novel method of point cloud data processing is proposed in this paper. This method is dedicated to the low-speed self-driving cars based on a lidar. The purpose of point cloud data processing is to reduce the amount of computation and its storage space of the 3D raw point cloud. Meanwhile, the processed point cloud is different from the ordinary 2D point cloud and it does not lose key spatial information points. The procedure is divided into three steps. Firstly, a valid data field is selected. And then the selected data field is filtered by the statistical outlier removal algorithm. Secondly, the point cloud filtered is downsampled by the voxel grid filter. Finally, the downsampled 3D point cloud data are processed to 2D data regularly. The experimental results show that the proposed method can retain the key spatial information points of the raw point cloud. The processed point cloud requires less computing resources and storage space, have higher reliability compared with the ordinary 2D data, reduce the algorithm complexity compared with 3D data, and be able to real time processing. The experimental vehicle equipped with a velodyne-16 sensor which outputs 3D point cloud data. As a comparison of the experiments, the vehicle also contains an rplidar_A2 sensor which outputs ordinary 2D data.