192 research outputs found
Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings
POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe
Human Crowdsourcing Data for Indoor Location Applied to Ambient Assisted Living Scenarios
In the last decades, the rise of life expectancy has accelerated the demand for new technological
solutions to provide a longer life with improved quality. One of the major areas
of the Ambient Assisted Living aims to monitor the elderly location indoors. For this purpose,
indoor positioning systems are valuable tools and can be classified depending on the
need of a supporting infrastructure. Infrastructure-based systems require the investment
on expensive equipment and existing infrastructure-free systems, although rely on the
pervasively available characteristics of the buildings, present some limitations regarding
the extensive process of acquiring and maintaining fingerprints, the maps that store the
environmental characteristics to be used in the localisation phase. These problems hinder
indoor positioning systems to be deployed in most scenarios.
To overcome these limitations, an algorithm for the automatic construction of indoor
floor plans and environmental fingerprints is proposed. With the use of crowdsourcing
techniques, where the extensiveness of a task is reduced with the help of a large undefined
group of users, the algorithm relies on the combination ofmultiple sources of information,
collected in a non-annotated way by common smartphones. The crowdsourced data is
composed by inertial sensors, responsible for estimating the users’ trajectories, Wi-Fi
radio and magnetic field signals. Wi-Fi radio data is used to cluster the trajectories into
smaller groups, each corresponding to specific areas of the building. Distance metrics
applied to magnetic field signals are used to identify geomagnetic similarities between
different users’ trajectories. The building’s floor plan is then automatically created, which
results in fingerprints labelled with physical locations.
Experimental results show that the proposed algorithm achieved comparable floor
plan and fingerprints to those acquired manually, allowing the conclusion that is possible
to automate the setup process of infrastructure-free systems. With these results, this
solution can be applied in any fingerprinting-based indoor positioning system
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