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Sensors 2018, 18(2), 480; https://doi.org/10.3390/s18020480

Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data

1
Software and Systems Engineering Research Group, Technical University of Munich, Boltzmannstr. 3, 85748 Garching bei München, Germany
2
Astronomical and Physical Geodesy, Technical University of Munich, Arcisstr. 21, 80333 Munich, Germany
3
Satellite Geodesy, Technical University of Munich, Arcisstr. 21, 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Received: 31 October 2017 / Revised: 15 January 2018 / Accepted: 25 January 2018 / Published: 6 February 2018
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Abstract

In this paper, we present our novel approach for the crowdsourced dynamic vertical mapping of buildings. For achieving this, we use the barometric sensor of smartphones to estimate altitude differences and the moment of the outdoor to indoor transition to extract reference pressure. We have identified the outdoor–indoor transition (OITransition) via the fusion of four different sensors. Our approach has been evaluated extensively over a period of 6 months in different humidity, temperature, and cloud-coverage situations, as well as over different hours of the day, and it is found that it can always predict the correct number of floors, while it can approximate the altitude with an average error of 0.5 m. View Full-Text
Keywords: indoor mapping; outdoor–indoor transition; CityGML; dynamic mapping; vertical mapping indoor mapping; outdoor–indoor transition; CityGML; dynamic mapping; vertical mapping
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Pipelidis, G.; Moslehi Rad, O.R.; Iwaszczuk, D.; Prehofer, C.; Hugentobler, U. Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data. Sensors 2018, 18, 480.

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