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A motion tracking solution for indoor localization using smartphones

: Guimaraes, Vania; Castro, Lourenco; Carneiro, Susana; Monteiro, Manuel; Rocha, Tiago; Barandas, Marília; Machado, João; Vasconcelos, Maria; Gamboa, Hugo; Elias, Dirk


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Instrumentation and Measurement Society:
International Conference on Indoor Positioning and Indoor Navigation, IPIN 2016 : 4-7 October 2016, Madrid, Spain
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-2425-4
ISBN: 978-1-5090-2424-7
ISBN: 978-1-5090-2426-1
International Conference on Indoor Positioning and Indoor Navigation (IPIN) <7, 2016, Madrid>
Fraunhofer AICOS ()
motion tracking; indoor localization; gait analysis; feature spot map; architectural feature; magnetic field; Wi-Fi; map matching; particle filter

As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution. The system is based on multiple gait-model based filtering techniques for accurate movement quantification in combination with an advanced fused positioning mechanism that leverages sequences of opportunistic observations towards an accurate localization process. Magnetic field fluctuations, Wi-Fi readings and movement data are incrementally matched with a feature spot map containing multi-dimensional spatially-related features that characterize the building. A novel and convenient way of mapping the architectural and environmental properties of buildings is also introduced, which avoids the burden normally associated with the process. The system has been evaluated by multiple users in open and crowded spaces where overall median localization errors between 1.11 m and 1.68 m were obtained. While the reported errors are already satisfactory in the context of indoor localization, improvements may be readily achieved through the inclusion of additional reference features. High accuracy performance coupled with an opportunistic and infrastructure-free approach creates a very desirable solution for the indoor localization market doge.