Print Email Facebook Twitter Automatic generation of indoor navigable space using a point cloud and its scanner trajectory Title Automatic generation of indoor navigable space using a point cloud and its scanner trajectory Author Staats, B.R. Diakite, A.A. (TU Delft Urban Data Science) Voûte, R.L. (TU Delft OLD Department of GIS Technology; TU Delft OLD Urban Design) Zlatanova, S. (TU Delft Urban Data Science) Date 2017 Abstract 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 However, in many cases the floor plans are out of date.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. Subject 3D laser scanningDynamic objectsFloorplanIndoorMLSNavigation spaceTrajectoryVoxel To reference this document use: http://resolver.tudelft.nl/uuid:d4e882cc-ddae-4070-8924-a7163717fef7 DOI https://doi.org/10.5194/isprs-annals-IV-2-W4-393-2017 Publisher ISPRS Source ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W4 Event ISPRS Geospatial Week 2017, 2017-09-18 → 2017-09-22, Wuhan, China Part of collection Institutional Repository Document type conference paper Rights © 2017 B.R. Staats, A.A. Diakite, R.L. Voûte, S. Zlatanova Files PDF isprs_annals_IV_2_W4_393_2017.pdf 2.44 MB Close viewer /islandora/object/uuid:d4e882cc-ddae-4070-8924-a7163717fef7/datastream/OBJ/view