Volume IV-4 | Copyright
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4, 141-146, 2018
https://doi.org/10.5194/isprs-annals-IV-4-141-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Sep 2018

19 Sep 2018

GEOMETRICAL NETWORK MODEL GENERATION USING POINT CLOUD DATA FOR INDOOR NAVIGATION

M. Nakagawa and R. Nozaki M. Nakagawa and R. Nozaki
  • Dept. of Civil Engineering, Shibaura Institute of Technology, Japan

Keywords: Geometrical network model, Indoor navigation, Point clouds, ISPRS benchmark

Abstract. 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.

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