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3D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning

Yan, J; Basiri, A; He, G; Hancock, C; (2019) 3D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning. IEEE Transactions on Instrumentation and Measurement (In press). Green open access

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Abstract

The vision-aided Pedestrian Dead Reckoning (PDR) systems have become increasingly popular, thanks to the ubiquitous mobile phone embedded with several sensors. This is particularly important for indoor use, where other indoor positioning technologies require additional installation or body-attachment of specific sensors. This paper proposes and develops a novel 3D Passive Vision-aided PDR system that uses multiple surveillance cameras and smartphone-based PDR. The proposed system can continuously track users’ movement on different floors by integrating results of inertial navigation and Faster R-CNN-based real-time pedestrian detection, while utilizing existing camera locations and embedded barometers to provide floor/height information to identify user positions in 3D space. This novel system provides a relatively low-cost and user-friendly solution, which requires no modifications to currently available mobile devices and also the existing indoor infrastructures available at many public buildings for the purpose of 3D indoor positioning. This paper shows the case of testing the prototype in a four-floor building, where it can provide the horizontal accuracy of 0.16m and the vertical accuracy of 0.5m. This level of accuracy is even better than required accuracy targeted by several emergency services, including the Federal Communications Commission (FCC). This system is developed for both Android and iOS-running devices.

Type: Article
Title: 3D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning
Open access status: An open access version is available from UCL Discovery
Publisher version: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?pu...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: altimetry, identification of persons, image processing, indoor environments, inertial navigation, position measurement, sensor fusion
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10071480
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