Abstract

Indoor navigation is important for various applications such as disaster management, building modeling, safety analysis etc. In the last decade, indoor environment has been a focus of wide research that includes development of indoor data acquisition techniques, 3D data modeling and indoor navigation. In this research, an automated method for 3D modeling of indoor navigation network has been presented. 3D indoor navigation modeling requires a valid 3D model that can be represented as a cell complex: a model without any gap or intersection such that two cells (e.g. room, corridor) perfectly touch each other. This research investigates an automated method for 3D modeling of indoor navigation network using a geometrical model of indoor building environment. In order to reduce time and cost of surveying process, Trimble LaserAce 1000 laser rangefinder was used to acquire indoor building data which led to the acquisition of an inaccurate geometry of building. The connection between surveying benchmarks was established using Delaunay triangulation. Dijkstra algorithm was used to find shortest path in between building floors. The modeling results were evaluated against an accurate geometry of indoor building environment which was acquired using highly-accurate Trimble M3 total station. This research intends to investigate and propose a novel method of topological navigation network modeling with a less accurate geometrical model to overcome the need of required an accurate geometrical model. To control the uncertainty of the calibration and of the reconstruction of the building from the measurements, interval analysis and homotopy continuation will be investigated in the near future.

Highlights

  • People spend almost 90 % of their life in indoor building environment (Klepeis et al 2001; Li and Lee 2010)

  • An automated 3D modeling of topological indoor navigation network is presented. In this approach, surveyed benchmarks are considered as dual node and generated 3D building model is considered as primal graph

  • Indoor navigation network is modeled using surveying benchmarks which are connected based on Delaunay triangulation

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Summary

Introduction

People spend almost 90 % of their life in indoor building environment (Klepeis et al 2001; Li and Lee 2010). Indoor building navigation model has different challenging issues such as suitability of 3D building models, indoor navigation networks, vertical and horizontal connectivity, which are required to be addressed. An automated 3D modeling of topological indoor navigation network is presented. In this approach, surveyed benchmarks are considered as dual node and generated 3D building model is considered as primal graph. Indoor navigation network is modeled using surveying benchmarks which are connected based on Delaunay triangulation. This paper is organized as follows: Background section presents background of indoor building navigation. Experimentation section elaborates literature review of indoor building surveying and current study of indoor topological navigation network. Result and performance analysis of proposed 3D indoor topological navigation network is presented in Result evaluation and performance analysis section.

Background
Result evaluation and performance analysis
Findings
Conclusion and future research
Full Text
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