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

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