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 status of global road users at transportation facilities. It relies greatly on the clustering algorithm for accurate and rapid data processing 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 algorithm can achieve a high processing efficiency with 24.4 ms 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.