Space subdivision in indoor mobile laser scanning point clouds based on scanline analysis

Yi Zheng, Michael Peter, Ruofei Zhong* (Corresponding Author), Sander Oude Elberink, Quan Zhou

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
87 Downloads (Pure)


Indoor space subdivision is an important aspect of scene analysis that provides essential information for many applications, such as indoor navigation and evacuation route planning. Until now, most proposed scene understanding algorithms have been based on whole point clouds, which has led to complicated operations, high computational loads and low processing speed. This paper presents novel methods to efficiently extract the location of openings (e.g., doors and windows) and to subdivide space by analyzing scanlines. An opening detection method is demonstrated that analyses the local geometric regularity in scanlines to refine the extracted opening. Moreover, a space subdivision method based on the extracted openings and the scanning system trajectory is described. Finally, the opening detection and space subdivision results are saved as point cloud labels which will be used for further investigations. The method has been tested on a real dataset collected by ZEB-REVO. The experimental results validate the completeness and correctness of the proposed method for different indoor environment and scanning paths.

Original languageEnglish
Article number1838
Pages (from-to)1-20
Number of pages20
JournalSensors (Switzerland)
Issue number6
Publication statusPublished - 5 Jun 2018


  • Indoor point clouds
  • Opening detection
  • Space subdivision
  • Trajectory


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