The presented method provides for a possibility to perform graph-based indoor localization, by comparing the topological structure embedded in a mesh model to the topological structure of a semantically rich reference model, specifically a BIM. However different in nature and structure, both input sources can be converted to a graph of similar calibre, such that they can be tested for a match. After a match between both graphs is found, the current position of the actor within the mesh model can be translated to the room found in the graph. This room is now connected to a room within the reference graph, for which the semantics are stored in the BIM. Returning these to the actor, a location description can be formed. Many indoor positioning methods have been developed, which can provide an actor with a relative geometric place. Most preferred are positioning systems not relying on a contingent system, which can be performed using a fusion of sensors embedded into a mobile device. Such a system found to perform sufficiently is VI-SLAM, simultaneously building a geometric place and tracking each pose and heading relatively. Its output is a mesh model, in which a viewshed of the indoor environment is built. Indoor localization however, entails finding a humanly interpretable qualitative description of a place, rather than a quantitative position in Euclidean space. In order to transcend such a position to a meaningful location, the context of an indoor environment has to be understood. As the interpretation of indoor space is different across the board, misalignment in data sources representing the indoor environment occurs. .

The distribution of people in buildings, the occupancy of lecture-, work- and study places and the accessibility of facilities are essential information at university campuses who have to cope with limited and even shrinking budgets and huge, rising real estate costs. Only little insight is gained in both occupancy and movement patterns with traditional counting techniques and user-based questionnaires. Management teams state that rooms and facilities are hardly used, though staff and students complain about overcrowded facilities and limited flexibility. Actual and accurate data on a 24/7 scale with high-granularity is missing.
In general Facility- and Asset Management lacks efficient methods for realtime, comprehensive and high-granularity information of location, capacity and use of tangible and intangible assets. Asset management could benefit from more detailed, more accurate and longitudinal data on assets, providing more insight into efficiency and effectiveness on different levels of scale through time.
Existing technologies could provide a platform delivering those required insights. Navigation- and communication technologies such as GNSS, Wi-Fi, Bluetooth, RFID can be used to ‘locate’ users, estimate intensities and reveal patterns of movement and patterns of use. For Asset management indoor localisation is essential.