Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2113-2119, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2113-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2113-2119, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2113-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

AUTOMATIC EXTRACTION OF ROAD MARKINGS FROM MOBILE LASER-POINT CLOUD USING INTENSITY DATA

L. Yao, Q. Chen, C. Qin, H. Wu, and S. Zhang L. Yao et al.
  • College of Survey and Geo-Informatics Tongji University, Shanghai 200092, China

Keywords: Laser Scanning, Road Marking, Point Cloud, High Precision Map, Adaptive Threshold Segmentation

Abstract. With the development of intelligent transportation, road’s high precision information data has been widely applied in many fields. This paper proposes a concise and practical way to extract road marking information from point cloud data collected by mobile mapping system (MMS). The method contains three steps. Firstly, road surface is segmented through edge detection from scan lines. Then the intensity image is generated by inverse distance weighted (IDW) interpolation and the road marking is extracted by using adaptive threshold segmentation based on integral image without intensity calibration. Moreover, the noise is reduced by removing a small number of plaque pixels from binary image. Finally, point cloud mapped from binary image is clustered into marking objects according to Euclidean distance, and using a series of algorithms including template matching and feature attribute filtering for the classification of linear markings, arrow markings and guidelines. Through processing the point cloud data collected by RIEGL VUX-1 in case area, the results show that the F-score of marking extraction is 0.83, and the average classification rate is 0.9.