Volume IV-2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 185-192, 2018
https://doi.org/10.5194/isprs-annals-IV-2-185-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 185-192, 2018
https://doi.org/10.5194/isprs-annals-IV-2-185-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  28 May 2018

28 May 2018

POLE-LIKE ROAD FURNITURE DETECTION IN SPARSE AND UNEVENLY DISTRIBUTED MOBILE LASER SCANNING DATA

F. Li1,2, M. Lehtomäki2, S. Oude Elberink1, G. Vosselman1, E. Puttonen2, A. Kukko2, and J. Hyyppä2 F. Li et al.
  • 1Faculty of Geo-Information Science and Earth Observation, University of Twente, the Netherlands
  • 2Finnish Geospatial Research Institute, Department of Remote Sensing and Photogrammetry, P.O. Box 15, 02431 Masala, Finland

Keywords: Pole-like Road Furniture Detection, Mobile Laser Scanning, Sparse, Unevenly Distributed

Abstract. Pole-like road furniture detection received much attention due to its traffic functionality in recent years. In this paper, we develop a framework to detect pole-like road furniture from sparse mobile laser scanning data. The framework is carried out in four steps. The unorganised point cloud is first partitioned. Then above ground points are clustered and roughly classified after removing ground points. A slicing check in combination with cylinder masking is proposed to extract pole-like road furniture candidates. Pole-like road furniture are obtained after occlusion analysis in the last stage. The average completeness and correctness of pole-like road furniture in sparse and unevenly distributed mobile laser scanning data was above 0.83. It is comparable to the state of art in the field of pole-like road furniture detection in mobile laser scanning data of good quality and is potentially of practical use in the processing of point clouds collected by autonomous driving platforms.