An indoor navigation network, simplified from complex building structures, is of primary importance for indoor navigation. Methods for generating such networks are becoming increasingly important as buildings are becoming larger and more complex. However, studies based on the geometry and topology of indoor spaces do not sufficiently account for the key and decision nodes of human wayfinding. This is mainly because the visible perception of pedestrians regarding the corridor path structure is not taken into consideration. In this study, we first propose the indoor visibility map (IVM), calculate the visibility of corridor space, and analyze the relationship between different types of corridors and quantitative visibility parameters of IVM. Based on the visibility analysis, corresponding network construction strategies are suggested for different types of indoor corridors that contain nodes and edges. The experimental results show that the proposed network identifies key locations in the corridor structure that have a decisive role in navigation. Furthermore, the network is simplified to some extent.

Indoor space information extraction is an important aspect of reconstruction for building information modeling and a necessary process for geographic information system from outdoor to indoor. Entity model extracting methods provide advantages in terms of accuracy for building indoor spaces, as compared with network and grid model methods, and the extraction results can be converted into a network or grid model. However, existing entity model extracting methods based on a search loop do not consider the complex indoor environment of a building, such as isolated columns and walls or cross-floor spaces. In this study, such complex indoor environments are analyzed in detail, and a new approach for extracting buildings’ indoor space information is proposed. This approach is based on indoor space boundary calculation, the Boolean difference for single-floor space extraction, relationship reconstruction, and cross-floor space extraction. The experimental results showed that the proposed method can accurately extract indoor space information from the complex indoor environment of a building with geometric, semantic, and relationship information. This study is theoretically important for better understanding the complexity of indoor space extraction and practically important for improving the modeling accuracy of buildings.

The map matching (MM) model plays an important role in revising the locations of floating car data (FCD) on a digital map. However, most existing MM models have multiple shortcomings, such as a low matching accuracy for complex roads, long running times, an inability to take full advantage of historical FCD information, and challenges in maintaining the topological adjacency and obeying traffic rules. To address these issues, an enhanced hidden Markov map matching (EHMM) model is proposed by adopting explicit topological expressions, using historical FCD information and introducing traffic rules. The EHMM model was validated against areal ground dataset at various sampling intervals and compared with the spatial and temporal matching model and the ordinary hidden Markov matching model. The empirical results reveal that the matching accuracy of the EHMM model is significantly higher than that of the reference models regarding real FCD trajectories at medium and high sampling rates. The running time of the EHMM model was notably shorter than those of the reference models. The matching results of the EHMM model retained topological adjacency and complied with traffic regulations better than the reference models.

Computer Aided Design (CAD) systems play an important role in the production of building designs that are used to assist building constructions. Thus, CAD building drawings contain a large amount of building data important for indoor navigation and other related analysis which are often done in BIM (Building Information Modeling). Therefore, the conversion of CAD-based building data to BIM is necessary to make the data in CAD useful for these applications. However, when mapping to the BIM model, there are semantic ambiguities which will have an important impact on the subsequent analysis of the information converted from CAD. This paper addresses these ambiguity problems by first examining the building information represented in CAD drawings and the mapping relationships between the building information in CAD drawings and that in BIM. Once these relationships are established, the semantic ambiguities existed in the mapping were identified and then corresponding constraints were developed to address these ambiguities. The experiment results show that the approach described in this paper can effectively eliminate the semantic ambiguity of the mapping from CAD to BIM and the converted BIM model can effectively support subsequent analysis.

Oblique imagery obtained from an Unmanned Aerial Vehicle (UAV) has been widely applied to large-scale three-dimensional (3D) reconstruction; however, the problems of partially missing model details caused by such factors as occlusion, distortion, and airflow, are still not well resolved. In this paper, a loop-shooting-aided technology is used to solve the problem of details loss in the 3D model. The use of loop-shooting technology can effectively compensate for losses caused by occlusion, distortion, or airflow during UAV flight and enhance the 3D model details in large scene- modeling applications. Applying this technology involves two key steps. First, based on the 3D modeling construction process, the missing details of the modeling scene are found. Second, using loop-shooting image sets as the data source, incremental iterative fitting based on aerotriangulation theory is used to compensate for the missing details in the 3D model. The experimental data used in this paper were collected from Yunnan Normal University, Chenggong District, Kunming City, Yunnan Province, China. The experiments demonstrate that loop-shooting significantly improves the aerotriangulation accuracy and effectively compensates for defects during 3D large-scale model reconstruction. In standard-scale distance tests, the average relative accuracy of our modeling algorithm reached 99.87% and achieved good results. Therefore, this technique not only optimizes the model accuracy and ensures model integrity, but also simplifies the process of refining the 3D model. This study can be useful as a reference and as scientific guidance in large-scale stereo measurements, cultural heritage protection, and smart city construction.

Presently, most models of crowd evacuations within a building are created manually. This is inefficient. To address this issue, this paper focuses on the differences and relationship between industry foundation classes (IFC) and the macroscopic network model for evacuation (MNME), and studies the mapping and construction methods used to convert entity objects into nodes and arcs in the MNME. Furthermore, corresponding attribute information in the MNME are established from IFC. Based on this process, the MNME is created and used as an input to EVACNET4 to obtain simulation results. Finally, the results, together with a 3D building model, are expressed in a unified environment. Our study results show that a network as well as the relevant attributes can be automatically generated from IFC, and can be adapted to different working conditions. The method proposed in this paper can automatically map the semantic information model to the MNME and the simulation result as well as the building model can be integrated in a Geographic Information System (GIS) environment.