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An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras

Sensors (Basel, Switzerland), 2018-07, Vol.18 (7), p.2229 [Peer Reviewed Journal]

2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2018 by the authors. 2018 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s18072229 ;PMID: 29997340

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  • Title:
    An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras
  • Author: Xiao, Aoran ; Chen, Ruizhi ; Li, Deren ; Chen, Yujin ; Wu, Dewen
  • Subjects: Airports ; Algorithms ; Art galleries & museums ; Cameras ; Computer vision ; deep learning ; Global navigation satellite system ; Indoor environments ; indoor positioning ; Infrastructure ; large indoor scene ; Location based services ; Machine learning ; Museums ; Navigation satellites ; Object recognition ; Shopping malls ; smartphone ; Smartphones ; Static objects
  • Is Part Of: Sensors (Basel, Switzerland), 2018-07, Vol.18 (7), p.2229
  • Description: The demand for location-based services (LBS) in large indoor spaces, such as airports, shopping malls, museums and libraries, has been increasing in recent years. However, there is still no fully applicable solution for indoor positioning and navigation like Global Navigation Satellite System (GNSS) solutions in outdoor environments. Positioning in indoor scenes by using smartphone cameras has its own advantages: no additional needed infrastructure, low cost and a large potential market due to the popularity of smartphones, etc. However, existing methods or systems based on smartphone cameras and visual algorithms have their own limitations when implemented in relatively large indoor spaces. To deal with this problem, we designed an indoor positioning system to locate users in large indoor scenes. The system uses common static objects as references, e.g., doors and windows, to locate users. By using smartphone cameras, our proposed system is able to detect static objects in large indoor spaces and then calculate the smartphones' position to locate users. The system integrates algorithms of deep learning and computer vision. Its cost is low because it does not require additional infrastructure. Experiments in an art museum with a complicated visual environment suggest that this method is able to achieve positioning accuracy within 1 m.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s18072229
    PMID: 29997340
  • Source: PubMed Central (Open access)
    DOAJ Directory of Open Access Journals
    Geneva Foundation Free Medical Journals at publisher websites
    AUTh Library subscriptions: ProQuest Central
    ROAD: Directory of Open Access Scholarly Resources
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