Abstract
The demand for easily deployable indoor localisation solutions has been growing. Although several systems have been proposed, their limitations regarding the high implementation costs hinder most of them to be widely used. Fingerprinting-based IPS (Indoor Positioning Systems) depend on characteristics pervasively available in buildings. However, such systems require indoor floor plans, which might not be available, as well as environmental fingerprints, that need to be collected through human resources intensive processes. To overcome these limitations, this paper proposes an algorithm for the automatic construction of indoor maps and fingerprints, solely depending on non-annotated crowdsourced data from smartphones. Our system relies on multiple gait-model based filtering techniques for accurate movement quantification in combination with opportunistic sensing observations. After the reconstruction of users’ movement with PDR (Pedestrian Dead Reckoning) techniques, Wi-Fi measurements are clustered to partition the trajectories into segments. Similar segments, which belong to the same cluster, are identified using an adaptive approach based on a geomagnetic field distance. Finally, the floor plans are obtained through a data fusion process. Merging the acquired environmental data using the obtained floor plan, fingerprints are aligned to physical locations. Experimental results show that the proposed solution achieved comparable floor plans and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free IPS.
Highlights
The dissemination of smart mobile devices, especially in the last decade, has supported the development of location-based services, which acquired a major role in our daily life
To obtain the difference between both maps, we evaluated the floor plan construction as a binary classification problem, where a cell belonging to the map is considered as a positive, while the contrary is considered as a negative
With most of the areas of the original map correctly reconstructed, we can affirm that our solution is able to automatically construct indoor floor plans for fingerprinting-based IPS, as it will be confirmed in the further analyses
Summary
The dissemination of smart mobile devices, especially in the last decade, has supported the development of location-based services, which acquired a major role in our daily life. Fingerprinting-based solutions rely in opportunistic readings from signals that are pervasively available in the majority of environments, which lowers their implementation costs This last type of system requires an extensive process to collect the updated buildings information, which may hinder its practical implementation, especially in large-scale applications. In the indoor location field, crowdsourcing can be used in an opportunistic way, where non-annotated data is collected from the users’ smartphones, such as they naturally move throughout the buildings [12] This data is processed to obtain the buildings’ floor plans and/or their environmental fingerprints. Taking into consideration the limitations of current IPS and the potentialities of crowdsourcing, we present an innovative algorithm to automatically construct indoor floor plans and environmental fingerprints It only relies on data collected in a non-annotated way with smart mobile devices, through crowdsourcing-similar techniques. The results obtained in the system evaluation are presented (Section 4) and the taken conclusions are drawn (Section 5)
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