Robust Lane Extraction From MLS Point Clouds Towards HD Maps Especially in Curve Road

Abstract

This article presents a semi-automated method to extract the lane features along the curved roads from mobile laser scanning (MLS) point clouds. The proposed method consists of four steps. After data pre-processing, a road edge detection algorithm is performed to distinguish road curbs and extract road surfaces. Then, textual and directional road markings such as arrows, symbols, and words, to inform drivers in necessary cases, are detected by intensity thresholding and conditional Euclidean clustering algorithms. Furthermore, lane markings are extracted by local intensity analysis and distance thresholding methods according to road design standards, because they are more regular along the road. Finally, centerline points on lanes are estimated based on the coordinates of extracted lane markings. Our method shows strong feasibility and robustness when creating high-definition (HD) maps from MLS data, by increasing the number of blocks in the curve and the distance threshold control in curved lane centerline extraction. Quantitative evaluations show that the average recall, precision, and F1-score obtained from four datasets for road marking extraction are 93.87%, 93.76%, and 93.73%, respectively. The generated lane centerlines are evaluated by overlaying them on manually labeled reference buffers from 4 cm resolution orthoimagery. The comparative study indicates that the proposed methods can achieve higher accuracy and robustness than most state-of-the-art methods.