Fast multi-resolution spatial clustering for 3D point cloud data

This paper presents a self-driving technology that relies on a built-in sensing system to detect traffic objects. The use of three-dimensional (3D) light detection and ranging (Lidar) to assist measurement and perception is feasible. As Lidar provides high-precision positioning and is not influenced by light intensity, it can be used in bad weather and effectively minimizes the number of accidents caused by self-driving vehicles. One of the intuitive clustering methods used by the modern 3D Lidar, is known as density-based spatial clustering of applications with noise (DBSCAN). DBSCAN uses a single distance threshold to classify the entire point cloud. However, due to the non-uniform scattering characteristics of 3D Lidar, the single threshold cannot meet the current clustering accuracy requirements. Moreover, the method is very time consuming. This study proposed a fast multiresolution clustering method to improve the accuracy and reduce the computations based on the DBSCAN algorithm. We used the 360 sweeping scanning pattern of 3D Lidar to search for the angular area near the point cloud and then used variable thresholds to cluster objects with different range zones. The experimental results demonstrated that the proposed method can effectively improve the accuracy and reduce the computation time required for 3D point cloud clustering.