Efficient and accurate indoor localization using landmark graphs

Gu, F, Kealy, A, Khoshelham, K and Shang, J 2016, 'Efficient and accurate indoor localization using landmark graphs', International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, vol. 41, no. B2, pp. 509-514.


Document type: Journal Article
Collection: Journal Articles

Title Efficient and accurate indoor localization using landmark graphs
Author(s) Gu, F
Kealy, A
Khoshelham, K
Shang, J
Year 2016
Journal name International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume number 41
Issue number B2
Start page 509
End page 514
Total pages 6
Publisher Copernicus GmbH
Abstract Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Fusing spatial information is an effective way to achieve accurate indoor localization with little or with no need for extra hardware. However, existing indoor localization methods that make use of spatial information are either too computationally expensive or too sensitive to the completeness of landmark detection. In this paper, we solve this problem by using the proposed landmark graph. The landmark graph is a directed graph where nodes are landmarks (e.g., doors, staircases, and turns) and edges are accessible paths with heading information. We compared the proposed method with two common Dead Reckoning (DR)-based methods (namely, Compass + Accelerometer + Landmarks and Gyroscope + Accelerometer + Landmarks) by a series of experiments. Experimental results show that the proposed method can achieve 73% accuracy with a positioning error less than 2.5 meters, which outperforms the other two DR-based methods.
Subject Navigation and Position Fixing
Keyword(s) Dead reckoning
Indoor localization
Landmark graph
Smartphones
Spatial information
DOI - identifier 10.5194/isprsarchives-XLI-B2-509-2016
Copyright notice ©
ISSN 1682-1750
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 5 Abstract Views  -  Detailed Statistics
Created: Thu, 31 Jan 2019, 11:26:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us