Keyframe based Large-Scale Indoor Localisation using Geomagnetic Field and Motion Pattern

Sen Wang, Hongkai Wen, Ronald Clark, Niki Trigoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)
34 Downloads (Pure)

Abstract

This paper studies indoor localisation problem by using low-cost and pervasive sensors. Most of existing indoor localisation algorithms rely on camera, laser scanner, floor plan or other pre-installed infrastructure to achieve sub-meter or sub-centimetre localisation accuracy. However, in some circumstances these required devices or information may be unavailable or too expensive in terms of cost or deployment. This paper presents a novel keyframe based Pose Graph Simultaneous Localisation and Mapping (SLAM) method, which correlates ambient geomagnetic field with motion pattern and employs low-cost sensors commonly equipped in mobile devices, to provide positioning in both unknown and known environments. Extensive experiments are conducted in large-scale indoor environments to verify that the proposed method can achieve high localisation accuracy similar to state-of-the-arts, such as vision based Google Project Tango.
Original languageEnglish
Title of host publication2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages1910-1917
Number of pages8
ISBN (Electronic)9781509037612
ISBN (Print)9781509037629
DOIs
Publication statusPublished - 1 Dec 2016

Publication series

Name Proceedings of the International Conference on Intelligent Robots and Systems
PublisherIEEE
ISSN (Print)2153-0866

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  • Cite this

    Wang, S., Wen, H., Clark, R., & Trigoni, N. (2016). Keyframe based Large-Scale Indoor Localisation using Geomagnetic Field and Motion Pattern. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1910-1917). ( Proceedings of the International Conference on Intelligent Robots and Systems). IEEE. https://doi.org/10.1109/IROS.2016.7759302