Multi-Modal Probabilistic Indoor Localization on a Smartphone

Co-authors

Frederike Dümbgen, Cynthia Oeschger, Mihailo Kolundzija, Emmanuel Girardin, Johan Leuenberger and Serge Ayer.


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Full text: Infoscience.
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Code: Will appear shortly.


Abstract

The satellite-based Global Positioning System (GPS) provides robust localization on smartphones outdoors. In indoor en- vironments, however, no system is close to achieving a similar level of ubiquity, with existing solutions offering different trade-offs in terms of accuracy, robustness and cost.

In this paper, we develop a multi-modal positioning system, targeted at smartphones, which aims to get the best out of each of its constituent modalities. More precisely, we combine Bluetooth low energy (BLE) beacons, round-trip-time (RTT) enabled WiFi access points and the smartphone’s inertial measurement unit (IMU) to provide a cheap robust localization system that, unlike fingerprinting methods, requires no pre-training. To do this, we use a probabilistic algorithm based on a conditional random field (CRF). We show how to incorporate sparse visual information to improve the accuracy of our system, using pose estimation from pre-scanned visual landmarks, to calibrate the system online.

Our method achieves an accuracy of around 2 meters on two realistic datasets, outperforming other distance-based localization approaches. We also compare our approach with an ultra-wideband (UWB) system. While we do not match the performance of UWB, our system is cheap, smartphone compatible and provides satisfactory performance for many applications.


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