Robust Extraction of Multiple-Type Support Positioning Devices in the Catenary System of Railway Dataset Based on MLS Point Clouds

Due to the external environment, the support positioning device (SPD) of the railway catenary is prone to break down. Therefore, the automatic detection of SPDs is of great research significance to ensure the safe operation of the railway system. However, the small-scale SPD expresses weak features due to the complexity of railway scenes. Therefore, we propose a new algorithm for automatically extracting multiple-type SPDs quickly. First, a preprocessing approach of equidistant scene segmentation is proposed based on trajectory points to divide the whole scene into several independent region units for batch processing. Then, a new point spacing-based spatial clustering of applications with noise (PSBSCAN) algorithm is proposed for the extraction of pillars in the pillar corridor of each region unit. Adopting point spacing as the core standard and the parallel iteration mode with only accessing edge points can reduce the influence of the uneven point cloud density and the large-scale point clouds. Next, to locate SPDs, the spatial relationship between the trajectory point, the pillar, and the SPD in the scene is introduced into the corresponding objects to build the spatial index with semantic information. Finally, contact wires installed on the SPD are filtered through multiple-level voxel segmentation based on octree to extract SPD accurately. Through the test of 10-km railway datasets, the average F-1 by the proposed method is 98.61%, and the average recognition rate of SPD is 99.65%, which shows that this method can achieve efficient extraction of multiple-type SPDs in railway scenes with different point densities.