Detection and reconstruction of static vehicle-related ground occlusions in point clouds from mobile laser scanning

Abstract

Vehicle-related ground occlusion is a common problem in MLS data. This study aims to design a detection and reconstruction method of static vehicle-related ground occlusion for MLS data. Ground extraction and vehicle segmentation are performed on the input point cloud data in advance. Then an α-shape boundary based on the prior vehicle geometry is designed to split non-ground empty area and ground occlusions. The occlusion is detected and matched with its corresponding vehicle using the relative position between them. This relative position relation and the height difference are used to detect the curb direction as the local road direction. Finally, the occlusions are reconstructed using two different methods: (1) a cell-based linear interpolation and (2) a point-based mathematical morphology. The methodology is tested by original scanned data and multi-temporal evaluation data captured from a residential area in Delft, the Netherlands with vehicle-mounted LiDAR sensors. The result shows that all occlusions cause by vehicles are successfully detected and the curb (road) direction is correctly extracted in most of the occluded areas. Both reconstructed results can visually integrate the original scanned data and recover the curb structure. The reconstruction errors of the linear interpolation method are 0.045 m in the z-axis direction and 0.051 m in total and the reconstruction errors of mathematical morphology are 0.048 m in the z-axis direction and 0.052 m in total.