Building Information Modeling (BIM) represents a new technology which supports the creation and management of a construction project for buildings. The most well-known open BIM standard, the Industry Foundation Classes (IFC), provides the detailed 3D geometry and rich spatial semantics. It has drawn growing attention of researchers working in geographical information science (GIS). In many related applications, indoor navigation greatly benefits from IFC since it requires the information about building interiors. IFC provides the semantics and geometry of building, and topological relationships of building elements can be readily derived. These three types of information are extensively used in the research on indoor navigation. In this paper we review the current approaches, applications and solutions developed for different aspects of indoor navigation, and summarize the latest status about the use of IFC-based BIM for indoor navigation. In the literature regarding both IFC and indoor navigation, a total of 87 related papers are identified and categorized. 30 papers relate to the generation of indoor navigation models; 20 papers are about model conversion and data modeling on the basis of the IFC schema; 13 papers refer to IFC applications with dynamic environments; 14 papers focus on the support of IFC to indoor localization; and the other papers involve various topics such as visualization and network queries based on IFC. We find that the most active research direction is to generate navigation models from IFC data, yet there is no robust automation method of the generation. By discussing the pros and cons of the current applications, this paper suggests three new directions for further research on the IFC supports to indoor navigation. That is, researchers can further investigate the vertical dimension of the 3D geometry in IFC, facilitate the transformation from IFC to standardized indoor navigation models and manage real-time data with IFC to reflect indoor changes.

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An indoor logical network qualitatively represents abstract relationships between indoor spaces, and it can be used for path computation. In this paper, we concentrate on the logical network that does not have notions for metrics. Instead, it relies on the semantics and properties of indoor spaces. A navigation path can be computed by deriving parameters from these semantics and minimizing them in routing algorithms. Although previous studies have adopted semantic approaches to build logical networks, routing methods are seldom elaborated. The main issue with such networks is to derive criteria for path computation using the semantics of spaces. Here, we present a routing mechanism that is based on a dedicated space classification and a set of routing criteria. The space classification reflects characteristics of spaces that are important for navigation, such as horizontal and vertical directions, doors and windows, etc. Six routing criteria are introduced, and they involve: (1) the spaces with the preferred semantics; and/or (2) their centrality in the logical network. Each criterion is encoded as the weights to the nodes or edges of the logical network by considering the semantics of spaces. Logical paths are derived by a traditional shortest-path algorithm that minimizes these weights. Depending on the building’s interior configuration, one criterion may result in several logical paths. Therefore, we introduce a priority ordering of criteria to support path selection and decrease the possible number of logical paths. We provide a proof-of-concept implementation for several buildings to demonstrate the usability of such a routing. The main benefit of this routing method is that it does not need geometric information to compute a path. The logical network can be created using verbal descriptions only, and this routing method can be applied to indoor spaces derived from any building subdivision.

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Extensive studies have concluded that the GNSS observations are heteroscedastic and physically correlated. Typically, the observation precisions are elevation dependent and between-frequency cross-correlations and time correlations exist. The influence of these stochastic characteristics on the GNSS positioning has been numerically well understood. However, their influence on the statistic tests of reliability has been rarely studied. We will systematically study the influence of GNSS stochastic characteristics on the statistic tests involved in reliability. With BeiDou as an example, the realistic elevation-dependent model, cross-correlations and time correlations are estimated. Then their impacts on the reliability are numerically analyzed by comparing with the empirical stochastic model where the stochastic characteristics, i.e., elevation-dependent precisions, cross-correlations and time correlations, are not adequately specified. Besides the overall test and w-test, the minimal detectable bias (MDB) and the separability of two w-test statistics are examined. The results show that the realistic elevation-dependent model will reduce probabilities of both false alarm and wrong detection for both overall test and w-test. Introducing the cross-correlations and time correlations properly can obtain the realistic MDBs together with reasonable separability measures, which all are helpful for users to make objective decisions in quality control of real GNSS applications.@en