Rapid Localization and Extraction of Street Light Poles in Mobile LiDAR Point Clouds: A Supervoxel-Based Approach

Abstract

This paper presents a supervoxel-based approach for automated localization and extraction of street light poles in point clouds acquired by a mobile LiDAR system. The method consists of five steps: preprocessing, localization, segmentation, feature extraction, and classification. First, the raw point clouds are divided into segments along the trajectory, the ground points are removed, and the remaining points are segmented into supervoxels. Then, a robust localization method is proposed to accurately identify the pole-like objects. Next, a localization-guided segmentation method is proposed to obtain pole-like objects. Subsequently, the pole features are classified using the support vector machine and random forests. The proposed approach was evaluated on three datasets with 1,055 street light poles and 701 million points. Experimental results show that our localization method achieved an average recall value of 98.8%. A comparative study proved that our method is more robust and efficient than other existing methods for localization and extraction of street light poles.