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.@en

Automatic generation of indoor navigable models is mostly based on 2D floor plans. However, in many cases the floor plans are out of date. Buildings are not always built according to their blue prints, interiors might change after a few years because of modified walls and doors, and furniture may be repositioned to the user's preferences. Therefore, new approaches for the quick recording of indoor environments should be investigated. This paper concentrates on laser scanning with a Mobile Laser Scanner (MLS) device. The MLS device stores a point cloud and its trajectory. If the MLS device is operated by a human, the trajectory contains information which can be used to distinguish different surfaces. In this paper a method is presented for the identification of walkable surfaces based on the analysis of the point cloud and the trajectory of the MLS scanner. This method consists of several steps. First, the point cloud is voxelized. Second, the trajectory is analysing and projecting to acquire seed voxels. Third, these seed voxels are generated into floor regions by the use of a region growing process. By identifying dynamic objects, doors and furniture, these floor regions can be modified so that each region represents a specific navigable space inside a building as a free navigable voxel space. By combining the point cloud and its corresponding trajectory, the walkable space can be identified for any type of building even if the interior is scanned during business hours.@en