Abstract: This paper presented a novel method for solid lanes extraction from Mobile laser scanning (MLS) point clouds. The proposed method firstly removed the off-ground point clouds and then calculated the scanning distance between the points of road surface and sensors. Then, the reflective intensity data of road surface were transformed into relative values to overcome the influence of the scanning distance, the points' density, abrasion and roughness of road surface block by block. After the intensity enhancement, road markings were separated from the road surface based on the k-means clustering and connected component. In order to deal with the problem of under-segmentation and over-segmentation caused by the adhesion of solid lines and stop lines or other entrance markings, some features of geometric shape and the spatial distribution were then used to refine the results of intensity segmentation by the Normalized Cuts. Finally, the semantic structure information of road markings was explored to separate the solid lines from other road markings like zebra crossings, dashed lines. Experiments were undertaken to evaluate the validities of the proposed method with four test data sets acquired from different MLS systems. Quantitative evaluations on four MLS data sets indicated that the proposed method achieved a Precision, Recall and F₁-Measure of 95.98%, 91.87% and 95.55%, respectively, which validated that the proposed method has achieved promising performance.

Key words: MLS points cloud, the intensity enhancement, road markings extraction, intensity segmentation, solid lanes detection