Abstract
Within this work we present an updated version of our indoor localization system for smartphones. The pedestrian’s position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. Our recently presented approximation scheme of the kernel density estimation allows to find an exact estimation of the current position, compared to classical methods like weighted-average. Absolute positioning information is given by a comparison between recent Wi-Fi measurements of nearby access points and signal strength predictions. Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access points, we use an optimization scheme based on a set of reference measurements to estimate a corresponding Wi-Fi model. This work provides three major contributions to the system. The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building’s walkable areas. The localization system is further updated by incorporating a threshold-based activity recognition using barometer and accelerometer readings, allowing for continuous and smooth floor changes. Within the scope of this work, we tackle problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures. For the latter, a simplification of our previous solution is presented for the first time, which does not involve any major changes to the particle filter. The goal of this work is to propose a fast to deploy localization solution, that provides reasonable results in a high variety of situations. To stress our system, we have chosen a very challenging test scenario. All experiments were conducted within a 13th century historic building, formerly a convent and today a museum. The system is evaluated using 28 distinct measurement series on four different test walks, up to 310 length and 10 duration. It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements. The introduced filtering methods allow for a real fail-safe system, while the optimization scheme enables an on-site setup-time of less then 120 for the building’s 2500 m2 walkable area.
Highlights
Setting up a reliable localization solution for a building is a challenging and time-consuming task, especially in environments that are not built with localization in mind or do not provide any wireless infrastructure or even both
Because of both, required memory amounts and inaccuracies of the graph-based model depending on the spacing, we developed a new basis for the transition step, that is still able to answer p(qt | qt−1, ot−1 ), but has a much smaller memory footprint while representing the real floor plan more accurately
Within this work we provided an extensive overview of our smartphone-based indoor localization system, providing both, previous advances and novel contributions
Summary
Setting up a reliable localization solution for a building is a challenging and time-consuming task, especially in environments that are not built with localization in mind or do not provide any wireless infrastructure or even both. Such scenarios are of special interest when old or historical buildings. In terms of European architecture, the problems emanating from these buildings worsen over time. In the scope of this work, we deployed an indoor localization system to a 13th century building. Since 1936, the 2500 m2 building acts as a museum of the medieval town Rothenburg ob der Tauber [1], Germany
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