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Pedestrian indoor positioning using smartphone multi-sensing, radio beacons, user positions probability map and IndoorOSM floor plan representation

  • Position awareness in unknown and large indoor spaces represents a great advantage for people, everyday pedestrians have to search for specific places, products and services. In this work a positioning solution able to localize the user based on data measured with a mobile device is described and evaluated. The position estimate uses data from smartphone built-in sensors, WiFi (Wireless Fidelity) adapter and map information of the indoor environment (e.g. walls and obstacles). A probability map derived from statistical information of the users tracked location over a period of time in the test scenario is generated and embedded in a map graph, in order to correct and combine the position estimates under a Bayesian representation. PDR (Pedestrian Dead Reckoning), beacon-based Weighted Centroid position estimates, map information obtained from building OpenStreetMap XML representation and probability map users path density are combined using a Particle Filter and implemented in a smartphone application. Based on evaluations, this work verifies that the use of smartphone hardware components, map data and its semantic information represented in the form of a OpenStreetMap structure provide 2.48 meters average error after 1,700 travelled meters and a scalable indoor positioning solution. The Particle Filter algorithm used to combine various sources of information, its radio WiFi-based observation, probability particle weighting process and the mapping approach allowing the inclusion of new indoor environments knowledge show a promising approach for an extensible indoor navigation system.

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Metadaten
Document Type:Conference Object
Language:English
Author:J. C. Aguilar Herrera, P. G. Plöger, A. Hinkenjann, J. Maiero, M. Flores, A. Ramos
Parent Title (English):2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Busan, South Korea, 27-30 Oct. 2014
First Page:636
Last Page:645
ISBN:978-1-4673-8054-6
DOI:https://doi.org/10.1109/IPIN.2014.7275538
Publisher:IEEE
Date of first publication:2015/09/28
Note:
This work was partially funded by the project PlaSMoNa (platform for social mobile navigation) in the Institute of Visual Computing of the Bonn-Rhein-Sieg University of Applied Science in partnership with the tarent solutions GmbH in Bonn, while funding is provided by the Federal Ministery of Economics and Technology (BMWI) within the so-called Central innovation programme for medium-sized businesses (ZIM)(grant number KF2644105).
Departments, institutes and facilities:Fachbereich Informatik
Institute of Visual Computing (IVC)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2015/09/29