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IJAT Vol.12 No.3 pp. 386-394
doi: 10.20965/ijat.2018.p0386
(2018)

Paper:

3D Modeling of Lane Marks Using a Combination of Images and Mobile Mapping Data

Jingxin Su*,†, Ryuji Miyazaki**, Toru Tamaki*, and Kazufumi Kaneda*

*Graduate School of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

Corresponding author

**Faculty of Psychological Science, Hiroshima International University, Hiroshima, Japan

Received:
September 14, 2017
Accepted:
March 30, 2018
Online released:
May 1, 2018
Published:
May 5, 2018
Keywords:
point cloud, mobile mapping system, region growing, scan-line algorithm, lane mark extraction
Abstract

When we drive a car, the white lines on the road show us where the lanes are. The lane marks act as a reference for where to steer the vehicle. Naturally, in the field of advanced driver-assistance systems and autonomous driving, lane-line detection has become a critical issue. In this research, we propose a fast and precise method that can create a three-dimensional point cloud model of lane marks. Our datasets are obtained by a vehicle-mounted mobile mapping system (MMS). The input datasets include point cloud data and color images generated by laser scanner and CCD camera. A line-based point cloud region growing method and image-based scan-line method are used to extract lane marks from the input. Given a set of mobile mapping data outputs, our approach takes advantage of all important clues from both the color image and point cloud data. The line-based point cloud region growing is used to identify boundary points, which guarantees a precise road surface region segmentation and boundary points extraction. The boundary points are converted into 2D geometry. The image-based scan line algorithm is designed specifically for environments where it is difficult to clearly identify lane marks. Therefore, we use the boundary points acquired previously to find the road surface region from the color image. The experiments show that the proposed approach is capable of precisely modeling lane marks using information from both images and point cloud data.

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Cite this article as:
Jingxin Su, Ryuji Miyazaki, Toru Tamaki, and Kazufumi Kaneda, “3D Modeling of Lane Marks Using a Combination of Images and Mobile Mapping Data,” Int. J. Automation Technol., Vol.12, No.3, pp. 386-394, 2018
Jingxin Su, Ryuji Miyazaki, Toru Tamaki, and Kazufumi Kaneda, Int. J. Automation Technol., Vol.12, No.3, pp. 386-394, 2018

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Last updated on May. 19, 2018