# An Overview of Indoor Positioning and Mapping Technology Standards
## 2 Related Work
### Indoor Mapping Technologies
[41] present an accuracy comparison among three different SLAM-based mapping systems: Matterport, SLAMER, and NAVIS in two different indoor scenes (an L-shaped corridor and an open style library). The results indicate SLAM-based indoor mapping systems with accurate LiDAR sensors can offer centimeter mapping accuracy in complex indoor environments. Point clouds acquired by laser scanners, depth cameras, and other sensors are a useful data source for the generation of 3D indoor models. [PERSON] et al. [42] present a deep learning approach to automatically generate indoor spatial data by parsing floor plans, which is time-saving and relatively low cost. Indoor modelling refers to the process supported by various tools and technologies involving the generation and the management of digital representations of the physical and the functional characteristics of indoor spaces; indoor maps can be regarded as the output of indoor surveying; and modelling and representation are the foundation of most indoor-based applications. Thereby, with the rapid development of smart cities, digital twins, and the metaverse, there is an increasing need for accurate and up-to-date spatial information and 3D building models of indoor environments. Recently, the ISPRS Working Group IV/5 initiated a benchmark standard on indoor modelling to evaluate and to document the performance of indoor modelling methods. The results are based on six-point cloud datasets for different indoor environments as captured by different sensors. Eleven submitted models are analyzed in [43]. Early surveys on indoor spatial models was done in [44], which from a context-aware perspective introduced cell-based, boundary-based, set-based, and graph-based models based on the requirements of localization, context-aware and adaptive navigation, location-aware communication, activity-oriented interactions, and spatial and behavioral analyses, as well as other efficiency-related requirements. As expected, hybrid spatial models that integrate different coexistent indoor space models combine the advantages of different models and thus effectively fulfill context-aware application requirements. Geographic information systems (GIS), which is designed to acquire, organize, manage, analyze, and visualize spatial data, can undoubtedly be used to study indoor modelling [9]. As well as visualization, indoor cartography or indoor map representation provide abstractions of the physical spaces based on indoor modelling and applications. A systematic review focused on the utilization of GIS in the analysis of indoor spaces was presented in [45]. Building information modeling (BIM), which aims to develop a methodology to manage essential building design and project data in digital format throughout the building's life-cycle [46], and 3D city models, which aim to represent urban environments with three-dimensional geometries of buildings and structures [47], are widely extended to indoor space modelling [48; 49; 50; 51]. Recently, indoor space modelling has benefited from augmented reality (AR) technologies for indoor scenes; and, the use of AR devices, such as the Microsoft HoloLens, now enhances the indoor navigation experience [52; 53]. With the development of IMM technologies and data quality improvement, for now, indoor mapping and modelling solutions are experiencing a real revolution, thus many modeling standards and file formats, such as IFC, MDR, CityGML, and IndoorGML emerged [54]. We will further discuss these modelling standards later.