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논문 기본 정보

자료유형
학술저널
저자정보
Claridades, Alexis Richard (University of the Philippines - Diliman) Lee, Jiyeong (University of Seoul) Blanco, Ariel (University of the Philippines – Diliman)
저널정보
한국측량학회 한국측량학회지 한국측량학회지 제36권 제5호
발행연도
2018.10
수록면
319 - 333 (15page)

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초록· 키워드

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As human beings spend more time indoors, and with the growing complexity of indoor spaces, more focus is given to indoor spatial applications and services. 3D topological networks are used for various spatial applications that involve navigation indoors such as emergency evacuation, indoor positioning, and visualization. Manually generating indoor network data is impractical and prone to errors, yet current methods in automation need expensive sensors or datasets that are difficult and expensive to obtain and process. In this research, a methodology for semi-automatically generating a 3D indoor topological model based on IndoorGML (Indoor Geographic Markup Language) is proposed. The concept of Shooting Point is defined to accommodate the usage of omnidirectional images in generating IndoorGML data. Omnidirectional images were captured at selected Shooting Points in the building using a fisheye camera lens and rotator and indoor spaces are then identified using image processing implemented in Python. Relative positions of spaces obtained from CAD (Computer-Assisted Drawing) were used to generate 3D node-relation graphs representing adjacency, connectivity, and accessibility in the study area. Subspacing is performed to more accurately depict large indoor spaces and actual pedestrian movement. Since the images provide very realistic visualization, the topological relationships were used to link them to produce an indoor virtual tour.

목차

Abstract
1. Introduction
2. Literature Review
3. Generation of IndoorGML Data from Omnidirectional Images
4. Experimental Implementation
5. Conclusions and Recommendations
References

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