Geometrical network model generation using point cloud data for indoor navigation

M. Nakagawa, R. Nozaki

研究成果: Conference article査読

6 被引用数 (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.

ジャーナルISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
出版ステータスPublished - 2018 9月 19
イベント2018 ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change - Delft, Netherlands
継続期間: 2018 10月 12018 10月 5

ASJC Scopus subject areas

  • 地球惑星科学(その他)
  • 環境科学(その他)
  • 器械工学


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