Point Cloud Clustering System with DBSCAN Algorithm for Low-Resolution LiDAR

LiDAR point cloud clustering is a crucial part of object detection and recognition. However, clustering enormous point cloud of LiDAR assigns a large processing load to an on-board device in a vehicle. In this paper, we propose point cloud clustering system with a density-based spatial clustering of applications with noise (DBSCAN) algorithm for low-resolution LiDAR, offloading clustering tasks and shortening the processing time. In order to verify the feasibility of the system, we implemented the point cloud clustering accelerator on a field programmable gate array (FPGA). The system demonstrated 39.5 times enhancement in the processing speed.