Artikel in Tagungsband INPROC-2014-03

Philipp, Damian; Baier, Patrick; Dibak, Christoph; Dürr, Frank; Rothermel, Kurt; Becker, Susanne; Peter, Michael; Fritsch, Dieter: MapGENIE: Grammar-enhanced Indoor Map Construction from Crowd-sourced Data.
In: Proceedings of the 12th IEEE International Conference on Pervasive Computing and Communications (PerCom 2014).
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik.
S. 139-147, englisch.
Budapest, Hungary: IEEE Computer Society Conference Publishing Services, 24. März 2014.
DOI: 10.1109/PerCom.2014.6813954.
Artikel in Tagungsband (Konferenz-Beitrag).
CR-Klassif.C.2.4 (Distributed Systems)
KeywordsPublic Sensing; Opportunistic Sensing; Indoor Mapping; Map Reconstruction; IMU; Grammar

While location-based services are already well established in outdoor scenarios, they are still not available in indoor environments. The reason for this can be found in two open problems: First, there is still no off-the-shelf indoor positioning system for mobile devices and, second, indoor maps are not publicly available for most buildings. While there is an extensive body of work on the first problem, the efficient creation of indoor maps remains an open challenge.

We tackle the indoor mapping challenge in our MapGENIE approach that automatically derives indoor maps from traces collected by pedestrians moving around in a building. Since the trace data is collected in the background from the pedestrians' mobile devices, MapGENIE avoids the labor-intensive task of traditional indoor map creation and increases the efficiency of indoor mapping. To enhance the map building process, MapGENIE leverages exterior information about the building and uses grammars to encode structural information about the building. Hence, in contrast to existing work, our approach works without any user interaction and only needs a small amount of traces to derive the indoor map of a building. To demonstrate the performance of MapGENIE, we implemented our system using Android and a foot-mounted IMU to collect traces from volunteers. We show that using our grammar approach, compared to a purely trace-based approach we can identify up to four times as many rooms in a building while at the same time achieving a consistently lower error in the size of detected rooms.

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Abteilung(en)Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Verteilte Systeme
Eingabedatum21. Januar 2014
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