Geometrical network model generation using point cloud data for indoor navigation

M. Nakagawa, R. Nozaki

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)


Three-dimensional indoor navigation requires various functions, such as the shortest path retrieval, obstacle avoidance, and secure path retrieval, for optimal path finding using a geometrical network model. Although the geometrical network model can be prepared manually, the model should be automatically generated using images and point clouds to represent changing indoor environments. Thus, we propose a methodology for generating a geometrical network model for indoor navigation using point clouds through object classification, navigable area estimation, and navigable path estimation. Our proposed methodology was evaluated through experiments using the benchmark of the International Society for Photogrammetry and Remote Sensing for indoor modeling. In our experiments, we confirmed that our methodology can generate a geometrical network model automatically.

Original languageEnglish
Pages (from-to)141-146
Number of pages6
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Issue number4
Publication statusPublished - 2018 Sept 19
Event2018 ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change - Delft, Netherlands
Duration: 2018 Oct 12018 Oct 5


  • Geometrical network model
  • ISPRS benchmark
  • Indoor navigation
  • Point clouds

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Environmental Science (miscellaneous)
  • Instrumentation


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