Fast-Spherical-Projection-Based Point Cloud Clustering Algorithm

Abstract Roadside LiDAR (light detection and ranging) is a solution to fill in the gaps for connected vehicles (CV) by detecting the sta- tus of global road users at transportation facilities. It relies greatly on the clustering algorithm for accurate and rapid data pro- cessing so as to ensure effectiveness and reliability. To contribute to better roadside LiDAR-based transportation facilities, this paper presents a fast-spherical-projection-based clustering algorithm (FSPC) for real-time LiDAR data processing with higher clustering accuracy and noise handling. The FSPC is designed to work on a spherical map which could be directly derived from the instant returns of a LiDAR sensor. A 2D-window searching strategy is specifically designed to accelerate the computation and alleviate the density variation impact in the LiDAR point cloud. The test results show the proposed algo- rithm can achieve a high processing efficiency with 24.4ms per frame, satisfying the real-time requirement for most common LiDAR applications (100 ms per frame), and it also ensures a high accuracy in object clustering, with 96%. Additionally, it is observed that the proposed FSPC allows a wider detection range and is more stable, tackling the surge in foreground points that frequently occurs in roadside LiDAR applications. Finally, the generality of the proposed FSPC indicates the proposed algorithm could also be implemented in other areas such as autonomous driving and remote sensing. Keywords data and data science, automatic vehicle detection and identification systems, computer vision, data science, remote sensing, vehicle detection Connected-vehicle (CV) technology is an emerging tech- nology to reduce crashes and increase energy efficiency in the transportation system, one which enables bi- directional communications between infrastructure and