amsys 19(18): e5

Research Article

Sensor Modalities and Fusion for Robust Indoor Localisation

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  • @ARTICLE{10.4108/eai.12-12-2019.162670,
        author={Michał Kozłowski and Ra\^{u}l Santos-Rodr\^{\i}guez and Robert J. Piechocki},
        title={Sensor Modalities and Fusion for Robust Indoor Localisation},
        journal={EAI Endorsed Transactions on Ambient Systems},
        volume={6},
        number={18},
        publisher={EAI},
        journal_a={AMSYS},
        year={2019},
        month={12},
        keywords={Localisation, Efficiency, Fusion, Robustness},
        doi={10.4108/eai.12-12-2019.162670}
    }
    
  • Michał Kozłowski
    Raúl Santos-Rodríguez
    Robert J. Piechocki
    Year: 2019
    Sensor Modalities and Fusion for Robust Indoor Localisation
    AMSYS
    EAI
    DOI: 10.4108/eai.12-12-2019.162670
Michał Kozłowski1,*, Raúl Santos-Rodríguez1, Robert J. Piechocki1
  • 1: Digital Health Engineering Group, University of Bristol, 1 Cathedral Square, Bristol, BS1 5DL
*Contact email: m.kozlowski@bristol.ac.uk

Abstract

The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.