Accurate Segmentation Method for Roadside Lampposts Based on Vehicle-Mounted Lidar Point Clouds

Abstract. The extraction of roadside lampposts constitutes a significant research focus within the domain of vehicular LiDAR point cloud object retrieval. Addressing the complexities inherent in discerning lampposts amidst convoluted vegetation in diverse roadway settings, this study introduces an innovative, progressive technique for the individualized extraction of lampposts utilizing vehicular LiDAR point clouds. The proposed method initiates with a bifurcation of the primary point cloud into terrestrial and aerial subsets via the Cloth Simulation Filter (CSF) algorithm. Subsequent processes involve the extraction of distinct lamppost structures from aerial point clouds through a methodology integrating elevation-normalized spatial partitioning and directional coverage analysis, thereby facilitating precise lamppost localization. The culmination of this process involves the refinement of lamppost point clouds through supervoxel clustering complemented by a non-discretization filter grounded in Principal Component Analysis (PCA). The efficacy of this novel approach is substantiated through empirical studies employing a LiDAR dataset encompassing extensive adhesive scenarios, whereupon comparative analysis with extant methodologies reveals its enhanced proficiency in isolating individual lampposts in complex environments.