Abstract
Indoor navigation networks are the foundation of indoor localization-based services. To build navigation graph networks of buildings, the medial axis is an effective way to represent the paths of indoor spaces, as medial axis-based navigation networks are consistent with common human cognition of paths and satisfy the requirements of spatial queries in most indoor map-matching algorithms. The existing methods for generating medial axis-based indoor navigation networks compute the modified medial axis of indoor spaces to represent indoor paths. However, these methods cannot deal with rooms with irregular shapes or generate detours. Especially, these methods fail to generate path lines to represent floor-to-floor paths accurately. To address these issues, this study develops a novel approach called an improved generation of straight skeletons-based navigation networks (I-GSSNN) to produce straight skeletons based indoor navigation graph networks. The straight skeleton is adopted in I-GSSNN to catch the medial axis of indoor spaces. The Industry Foundation Classes (IFC) model, a widely-used format of Building Information Modeling (BIM), is used as input in the I-GSSNN. The I-GSSNN is a workflow that includes constructing path lines of corridors, connecting doors to corridors, generating floor-to-floor path lines, and removing redundant lines. Compared with existing methods, the I-GSSNN can handle rooms with any polygon shape and produces fewer detours. Novel algorithms are developed in the I-GSSNN to generate path lines for accurately representing stairs and elevators. An educational building is used as a case study to validate the result of I-GSSNN. The case study indicates that the I-GSSNN can generate a complete three-dimensional (3D) navigation network of the educational building. In addition, the accuracy test shows that the route length based on the generated navigation networks is very close to the real length. Significantly, the results show that the I-GSSNN can accurately generate full 3D navigation graph networks of buildings that can serve as indoor maps for indoor route planning and navigation and can also be used in code checking and pedestrian circulation analysis during building design.
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