Automatic Generation of 3D Indoor Navigation Networks from Building Information Modeling Data Using Image Thinning

Navigation networks are a common form of indoor map that provide the basis for wide range location-based services, intelligent tasks robots, and three-dimensional (3D) geographic information systems. The majority current navigation manually modeled, resulting in laborious fallible process. Building Information Modeling (BIM) captures design information, allowing automated generation maps. Most existing BIM-based systems floor-level wayfinding rely on well-defined spatial semantics, do not adapt well to buildings with irregular 3D shapes, which can make cross-floor path difficult. This research introduces an innovative approach generating automatically from BIM data using image thinning, is referred as GINIT. Firstly, GINIT extracts grid-based maps floors only two types i.e., slabs doors. Secondly, paths building components by projecting forms onto 2D image, thinning capture projection path, crossing over routes restore path. Finally, demonstrate effectiveness GINIT, experiments were conducted three real-world multi-floor buildings, evaluating its performance across eight cross-layer architectural component. overcomes dependency space definitions network schemes introducing thinning. Due adaptability any binary capable diverse shapes. Moreover, studies extraction mainly use geometry theory, while this study first generate theory. results will offer unique perspective foster exploration imaging theory applications BIM.