Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
AbstractIn 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
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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.
Pipelidis G, Moslehi Rad OR, Iwaszczuk D, Prehofer C, Hugentobler U. Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data. Sensors. 2018; 18(2):480.Chicago/Turabian Style
Pipelidis, Georgios; Moslehi Rad, Omid R.; Iwaszczuk, Dorota; Prehofer, Christian; Hugentobler, Urs. 2018. "Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data." Sensors 18, no. 2: 480.
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