Research on indoor navigation models mainly focuses on geometric and logical models .The models are enriched with specific semantic information which supports localisation, navigation and guidance. Geometric models provide information about the structural (physical) distribution of spaces in a building, while logical models indicate relationships (connectivity and adjacency) between the spaces. In many cases geometric models contain virtual subdivisions to identify smaller spaces which are of interest for navigation (e.g. reception area) or make use of different semantics. The geometric models are used as basis to automatically derive logical models. However, there is seldom reported research on how to automatically realize such geometric models from existing building data (as floor plans) or indoor standards (CityGML LOD4 or IFC). In this paper, we present our experiments on automatic creation of logical models from floor plans and CityGML LOD4. For the creation we adopt the Indoor Spatial Navigation Model (INSM) which is specifically designed to support indoor navigation. The semantic concepts in INSM differ from daily used notations of indoor spaces such as rooms and corridors but they facilitate automatic creation of logical models.
ABSTRACT:In this paper, we leverage spatial model to process indoor localization results and then improve the track consisting of measured locations. We elaborate different parts of spatial model such as geometry, topology and semantics, and then present how they contribute to the processing of indoor tracks. The initial results of our experiment reveal that spatial model can support us to overcome problems such as tracks intersecting with obstacles and unstable shifts between two location measurements. In the future, we will investigate more exceptions of indoor tracking results and then develop additional spatial methods to reduce errors of indoor tracks.
ABSTRACT:This paper introduces and compares two types of GML-based data standards for indoor location-based services, i.e., IndoorGML and IndoorLocationGML. By elaborating the advantages of the both standards and their data models, we conclude that the two data standards are complementary to each other. A jointed data model is presented to show the integration of the two standards. IndoorGML can supply subdivision of building for data of IndoorLocationGML, and the semantics of locations defined in IndoorLocationGML can be added to IndoorGML. By proposing two use cases, we take the initiative in attempting to combine the use of the two standards. The first case is to collect details from files of the two standards for an indoor path; the second one is to generate verbal directions for indoor guidance from files of the two standards. Some future work is given for further development, such as automatic integration of separate data from both standards.
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