Road surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model

Abstract

Roads are one of the main characteristics of cities, and their data should be updated periodically. In this study, a new automatic method is proposed for extracting road surface information and road inventory from a Mobile LiDAR System-based point cloud. The proposed method consists of four steps. First, a three-dimensional point cloud is acquired using the MLS raw data. To improve the extraction accuracy, irrelevant points are removed from the point cloud. Piecewise linear models are used in the third step to classify the road surface. Road geometric characteristics such as centerline, profile, cross-section, and cross slope are extracted in the final step. The manually obtained road boundary is compared with the extracted road boundary to assess the classification results. Completeness, correctness, quality, and accuracy measures are range from 97% to 99%. When comparing these measures with previous studies, the proposed method produces one of the highest ones.

A hybrid framework for robust dynamic 3D point clouds removal

“…Nonetheless, the scope of generalization inherent to this method exhibits certain limitations, showcasing optimal performance primarily within the context of the trained environment. Instead of learning-based methods, non-learning-based methods [14][15][16][17] can also achieve high performance in ground segmentation. Fischler and Bolles et al [18].…”