A novel approach for dynamic vertical indoor mapping through crowd-sourced smartphone sensor data

In this paper we present our developed and evaluated method for the dynamic mapping of the vertical characteristics inside a building. For achieving that, we extract data from smart-phone sensors and use those data for altitude estimation via the barometric formula. We introduce a novel approach for the extraction of reference pressure during the outdoor-to-indoor-transition of the user inside a building, which is identified through sensor fusion. A combination of machine learning techniques is used for the identification of the number of floors and the unsupervised classification of the altitude of each floor. As far as we know, this is the first system able of mapping vertical characteristics inside a building autonomously. Finally, enhancements on the CityGML model are made for mapping those characteristic by following its given standards.