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Sensors 2018, 18(7), 2229; https://doi.org/10.3390/s18072229

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

1
,
1,2,* , 1,2,* , 3
and
1,2
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
3
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Received: 4 June 2018 / Revised: 8 July 2018 / Accepted: 9 July 2018 / Published: 11 July 2018
(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)
Full-Text   |   PDF [7016 KB, uploaded 11 July 2018]   |  

Abstract

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. View Full-Text
Keywords: indoor positioning; smartphone; large indoor scene; computer vision; deep learning indoor positioning; smartphone; large indoor scene; computer vision; deep learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Xiao, A.; Chen, R.; Li, D.; Chen, Y.; Wu, D. An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras. Sensors 2018, 18, 2229.

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