Journal cover Journal topic
Advances in Cartography and GIScience of the ICA
Journal topic
Volume 1
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 1, 12, 2019
https://doi.org/10.5194/ica-adv-1-12-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 1, 12, 2019
https://doi.org/10.5194/ica-adv-1-12-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Jul 2019

03 Jul 2019

Automated Extraction of Driving Lines from Mobile Laser Scanning Point Clouds

Lingfei Ma1, Tianyu Wu1, Ying Li1, Jonathan Li1,2,3, Yiping Chen3, and Michael Chapman4 Lingfei Ma et al.
  • 1Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
  • 2Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
  • 3Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen, FJ 361005, China
  • 4Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada

Keywords: HD map, driving line, mobile laser scanning, point cloud

Abstract. This paper presents a novel approach to automated generation of driving lines from mobile laser scanning (MLS) point cloud data. The proposed method consists of three steps: road surface segmentation, road marking extraction and classification, and driving line generation. The voxel-based upward-growing algorithm was firstly used to extract ground points from the raw MLS point clouds followed by segmentation of road surface using a region-growing algorithm. Then, the statistical outlier removal filter was applied to separate and refine the road marking points followed by extracting and classifying the lane markings based on the geometric features of different road markings using empirical hierarchical decision trees. Finally, land node structures were constructed followed by generation of driving lines using a curve-fitting algorithm. The proposed method was tested on both circular road sections and irregular intersections. The smoothing spline curve fitting model was tested on the circular road sections, while the Catmull-Rom spline with five control points was used to generate the driving lines at road intersections. The overall performance of the proposed algorithms is promising with 96.0% recall, 100.0% precision, and 98.0% F1-score for the lane marking extraction specifically. Most significantly, the validation results demonstrate that the driving lines can be effectively generated within 20 cm-level localization accuracy at an average of 3.5% miscoding using MLS point clouds, which meets the requirement of localization accuracy of fully autonomous driving functions. The results demonstrate the proposed methods can successfully generate road driving lines in the test datasets to support the development of high-definition maps.

Publications Copernicus
Download
Citation