EXTRACTION OF THE 3D FREE SPACE FROM BUILDING MODELS FOR INDOOR NAVIGATION
For several decades, indoor navigation has been exclusively investigated in a 2D perspective, based on floor plans, projection and other 2D representations of buildings. Nevertheless, 3D representations are closer to our reality and offer a more intuitive description of the space configuration. Thanks to recent advances in 3D modelling, 3D navigation is timidly but increasingly gaining in interest through the indoor applications. But, because the structure of indoor environment is often more complex than outdoor, very simplified models are used and obstacles are not considered for indoor navigation leading to limited possibilities in complex buildings. In this paper we consider the entire configuration of the indoor environment in 3D and introduce a method to extract from it the actual navigable space as a network of connected 3D spaces (volumes). We describe how to construct such 3D free spaces from semantically rich and furnished IFC models. The approach combines the geometric, the topological and the semantic information available in a 3D model to isolate the free space from the rest of the components. Furthermore, the extraction of such navigable spaces in building models lacking of semantic information is also considered. A data structure named combinatorial maps is used to support the operations required by the process while preserving the topological and semantic information of the input models.