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


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

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

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.

  1. [1] I. Puente, H. González-Jorge, J. Martínez-Sánchez, and P. Arias, “Review of mobile mapping and surveying technologies,” Measurement, Vol.46, No.7, pp. 2127-2145, 2013.
  2. [2] K. Ishikawa, Y. Amano, T. Hashizume, J. Takiguchi, and N. Kajiwara, “A Mobile Mapping System for Precise Road Line Localization Using a Single Camera and 3D Road Model,” J. of Robotics and Mechatronics, Vol.19, No.2, pp. 174-180, 2007.
  3. [3] S. Cavegn and N. Haala, “Image-Based Mobile Mapping for 3D Urban Data Capture,” Photogrammetric Engineering & Remote Sensing, Vol.82, No.12, pp. 925-933, 2016.
  4. [4] S. Murray, S. Haughey, C. Deegan, M. Brogan, C. Fitzgerald, and S. McLoughlin, “Mobile mapping system for the automated detection and analysis of road delineation,” IET Intelligent Transport Systems, Vol.5, No.4, pp. 221-230, 2011.
  5. [5] N. Sairam, S. Nagarajan, and S. Ornitz, “Development of Mobile Mapping System for 3D Road Asset Inventory,” Sensors, Vol.16, No.3, p. 367, 2016.
  6. [6] Z. Shen, X. Chen, X. Tang, and H. Zhang, “Road Damage Feature Extraction in Image Based on Fractal Dimension,” Applied Mechanics and Materials, Vol.256-259, pp. 2971-2975, 2012.
  7. [7] M. Lehtomäki, A. Jaakkola, J. Hyyppä, A. Kukko, and H. Kaartinen, “Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data,” Remote Sensing, Vol.2, No.3, pp. 641-664, 2010.
  8. [8] H. Yokoyama, H. Date, S. Kanai, and H. Takeda, “Pole-like Objects Recognition from Mobile Laser Scanning Data using Smoothing and Principal Component Analysis,” ISPRS – Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.512, pp. 115-120, 2012.
  9. [9] A. Boyko and T. Funkhouser, “Extracting roads from dense point clouds in large scale urban environment,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.66, No.6, pp. S2-S12, 2011.
  10. [10] G. Mastorakis and E. Davies, “Improved line detection algorithm for locating road lane markings,” Electronics Letters, Vol.47, No.3, p. 183, 2011.
  11. [11] X. Li, X. Fang, C. Wang, and W. Zhang, “Lane Detection and Tracking Using a Parallel-snake Approach,” J. of Intelligent & Robotic Systems, Vol.77, No.3-4, pp. 597-609, 2014.
  12. [12] C.-F. Wu, C.-J. Kin, and C.-Y. Lee, “Applying a Functional Neurofuzzy Network to Real-Time Lane Detection and Front-Vehicle Distance Measurement,” IEEE Trans. on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol.42, No.4, pp. 577-589, 2012.
  13. [13] P. Kumar, C. McElhinney, P. Lewis, and T. McCarthy, “Automated road markings extraction from mobile laser scanning data,” Int. J. of Applied Earth Observation and Geoinformation, Vol.32, pp. 125-137, 2014.
  14. [14] B. Yang, L. Fang, Q. Li, and J. Li, “Automated Extraction of Road Markings from Mobile Lidar Point Clouds,” Photogrammetric Engineering & Remote Sensing, Vol.78, No.4, pp. 331-338, 2012.
  15. [15] L. Yan, H. Liu, J. Tan, Z. Li, H. Xie, and C. Chen, “Scan Line Based Road Marking Extraction from Mobile LiDAR Point Clouds,” Sensors, Vol.16, No.6, p. 903, 2016.
  16. [16] Y. N. Lien, T. A. Teo, C. T. Chen, and P. Y. Huang, “Recognizing the Road Points and Road Marks From Mobile LIDAR Point Clouds,” Remote Sensing Asian Conf. 33rd 2012, Vol.2, No.4, pp. 1054-1059, 2012.
  17. [17] R. Miyazaki, M. Yamamoto, E. Hanamoto, H. Izumi, and K. Harada, “A line-based approach for precise extraction of road and curb region from mobile mapping data,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.5, pp. 243-250, 2014.
  18. [18] R. Miyazaki, M. Yamamoto, and K. Harada, “Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution,” Int. J. of Automation Technology, Vol.11, No.4, pp. 657-665, 2017.
  19. [19] V. S. Bottazzi, P. V. Borges, and B. Stantic, “Adaptive regions of interest based on HSV histograms for lane marks detection,” Robot Intelligence Technology and Applications 2, pp. 677-687, Springer Int. Publishing, 2014.
  20. [20] C. Mu and X. Ma, “Lane Detection Based on Object Segmentation and Piecewise Fitting,” TELKOMNIKA Indonesian J. of Electrical Engineering, Vol.12, No.5, 2014.
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

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on May. 19, 2018