The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1007–1013, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1007-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1007–1013, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1007-2019

  05 Jun 2019

05 Jun 2019

MLS POINT CLOUD SEGMENTATION BASED ON FEATURE POINTS OF SCANLINES

R. Honma1, H. Date2, and S. Kanai2 R. Honma et al.
  • 1Asia Air Survey Co.,Ltd., 215-0004 Kawasaki-shi, Kanagawa, Japan
  • 2Graduate School of Information Science and Technology, Hokkaido University, 060-0814 Sapporo, Japan

Keywords: Point Cloud, MLS, Scanline, Segmentation, Road

Abstract. Point clouds acquired using Mobile Laser Scanning (MLS) are applied to extract road information such as curb stones, road markings, and road side objects. In this paper, we present a scanline-based MLS point cloud segmentation method for various road and road side objects. First, end points of the scanline, jump edge points, and corner points are extracted as feature points. The feature points are then interpolated to accurately extract irregular parts consisting of irregularly distributed points such as vegetation. Next, using a point reduction method, additional feature points on a smooth surface are extracted for segmentation at the edges of the curb cut. Finally, points between the feature points are extracted as flat segments on the scanline, and continuing feature points are extracted as irregular segments on the scanline. Furthermore, these segments on the scanline are integrated as flat or irregular regions. In the extraction of the feature points, neighboring points based on the spatial distance are used to avoid being influenced by the difference in the point density. Based on experiments, the effectiveness of the proposed method was indicated based on an application to an MLS point cloud.