Segmentation of Communication Cabinets Based on Point Cloud Coverage From LiDAR Point Cloud

The settlement and tilt of a communication base station’s cabinet can harm electronic components, affecting stable operation. A 3-D model measures cabinet deformation using light detection and ranging (LiDAR) data, facilitating timely maintenance. While limited studies address cabinet segmentation, existing methods, relying on line or plane detection, face challenges with interference from objects sharing features with the cabinet. This letter proposes a cabinet point cloud segmentation method that combines cluster analysis and plane point cloud coverage (PCC). The method preprocesses the point cloud, analyzes the differences in normal vector characteristics in different points neighborhoods, segments the point cloud based on the given normal vector value, performs Euclidean clustering, and extracts the planar point cloud using the RANSAC algorithm to judge if it meets the characteristics of the cabinet. The proposed method is validated with nine datasets, demonstrating a 98.23% average F1-score. Results confirm the algorithm’s accuracy in extracting cabinet point cloud, showcasing its versatility compared to common algorithms.