3D crack skeleton extraction from mobile LiDAR point clouds

This paper presents a novel algorithm for extracting 3D crack skeletons from 3D point clouds acquired by a mobile Light Detection and Ranging (LiDAR) system. This algorithm uses intensity information of cloud clouds to identify pavement cracks that usually exhibit lower intensities compared to their surroundings. First, crack candidates are extracted by applying the Otsu thresholding algorithm. Then, a spatial density filter is used to remove outliers. Next, crack points are grouped into crack-lines using a Euclidean distance clustering method. Finally, crack skeletons are extracted based on an L1-medial skeleton extraction method. The proposed algorithm has been tested on a set of mobile LiDAR point clouds acquired by a state-of-the-art RIEGL VMX-450 mobile LiDAR system. The results demonstrate the efficiency and reliability of the proposed algorithm in extracting 3D crack skeletons.