Learning Hierarchical Features for Automated Extraction of Road Markings From 3-D Mobile LiDAR Point Clouds

Abstract

This paper presents a novel method for automated extraction of road markings directly from three dimensional (3-D) point clouds acquired by a mobile light detection and ranging (LiDAR) system. First, road surface points are segmented from a raw point cloud using a curb-based approach. Then, road markings are directly extracted from road surface points through multisegment thresholding and spatial density filtering. Finally, seven specific types of road markings are further accurately delineated through a combination of Euclidean distance clustering, voxel-based normalized cut segmentation, large-size marking classification based on trajectory and curb-lines, and small-size marking classification based on deep learning, and principal component analysis (PCA). Quantitative evaluations indicate that the proposed method achieves an average completeness, correctness, and F-measure of 0.93, 0.92, and 0.93, respectively. Comparative studies also demonstrate that the proposed method achieves better performance and accuracy than those of the two existing methods.