NDLI: Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning

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Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning

Content Provider	ACM Digital Library
Author	Bobkov, Dmytro Schroth, Georg Hilsenbeck, Sebastian Huitl, Robert Steinbach, Eckehard
Abstract	We propose a graph-based, low-complexity sensor fusion approach for ubiquitous pedestrian indoor positioning using mobile devices. We employ our fusion technique to combine relative motion information based on step detection with WiFi signal strength measurements. The method is based on the well-known particle filter methodology. In contrast to previous work, we provide a probabilistic model for location estimation that is formulated directly on a fully discretized, graph-based representation of the indoor environment. We generate this graph by adaptive quantization of the indoor space, removing irrelevant degrees of freedom from the estimation problem. We evaluate the proposed method in two realistic indoor environments using real data collected from smartphones. In total, our dataset spans about 20 kilometers in distance walked and includes 13 users and four different mobile device types. Our results demonstrate that the filter requires an order of magnitude less particles than state-of-the-art approaches while maintaining an accuracy of a few meters. The proposed low-complexity solution not only enables indoor positioning on less powerful mobile devices, but also saves much-needed resources for location-based end-user applications which run on top of a localization service.
Starting Page	147
Ending Page	158
Page Count	12
File Format	PDF QT / MOV
ISBN	9781450329682
DOI	10.1145/2632048.2636079
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ubiquitous localization Indoor navigation Location-based services Indoor positioning Graph-based sensor fusion Mobile computing Particle filter
Content Type	Video Text
Resource Type	Article

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