Modelling 3D Topographic Space Against Indoor Navigation Requirements

Brown, Gavin; Nagel, Claus; Zlatanova, Sisi; Kolbe, Thomas H.

doi:10.1007/978-3-642-29793-9_1

Cited by 47 publications

(49 citation statements)

References 4 publications

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“…Li et al(2015) propose an effective method for extracting the detailed semantic semantics, functional information and interrelated information of BIM model (Li et al, 2015). Brown et al(2013) suggest topological space information still should be integrated in the indoor navigation, although collection and processing of sensor information, and positioning algorithms have developed rapidly. It describes these requirements and build 3D topological space model based on them.…”

Section: Related Workmentioning

confidence: 99%

“…BIM and CityGML LoD4 are investigated for path planning. Extraction of building geometric information have been completed in most studies, but some properties of entities and the semantic information between them have been lost, which are critical in the navigation (Teo and Cho, 2016;Hamieh, 2017;Brown, 2013). Research have already shown that semantic, topology and other information helps improve the accuracy of navigation.…”

Section: Introductionmentioning

confidence: 99%

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Bim-Based Indoor Path Planning Considering Obstacles

Man

Wei

Zlatanova

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:At present, 87% of people's activities are in indoor environment; indoor navigation has become a research issue. As the building structures for people's daily life are more and more complex, many obstacles influence humans' moving. Therefore it is essential to provide an accurate and efficient indoor path planning. Nowadays there are many challenges and problems in indoor navigation. Most existing path planning approaches are based on 2D plans, pay more attention to the geometric configuration of indoor space, often ignore rich semantic information of building components, and mostly consider simple indoor layout without taking into account the furniture. Addressing the above shortcomings, this paper uses BIM (IFC) as the input data and concentrates on indoor navigation considering obstacles in the multi-floor buildings. After geometric and semantic information are extracted, 2D and 3D space subdivision methods are adopted to build the indoor navigation network and to realize a path planning that avoids obstacles. The 3D space subdivision is based on triangular prism. The two approaches are verified by the experiments.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bim-Based Indoor Path Planning Considering Obstacles

Man

Wei

Zlatanova

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The standard is developed with the aim to reflect user specifications (Brown et al 2013 and provide a general framework for space definition (Figure 1). Prior IndoorGML, research has already indicated that a subdivision of indoor spaces according to human perception will improve the indoor guidance and navigation (e.g.…”

Section: * Corresponding Authormentioning

confidence: 99%

Ladm and Indoorgml for Support of Indoor Space Identification

Zlatanova

Oosterom

Lee

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Guidance and security in large public buildings such as airports, museums and shopping malls requires much more information that traditional 2D methods offer. Therefore 3D semantically-reach models have been actively investigated with the aim to gather knowledge about availability and accessibility of spaces. Spaces can be unavailable to specific users because of plenty of reasons: the 3D geometry of spaces (too low, too narrow), the properties of the objects to be guided to a specific part of the building (walking, driving, flying), the status of the indoor environment (e.g. crowded, limited light, under reconstruction), property regulations (private areas), security considerations and so on.However, such information is not explicitly avaible in the existing 3D semantically-reach models. IFC and CityGML are restricted to architectural building components and provide little to no means to describe such properties. IndoorGML has been designed to establish a generic approach for space identification allowing a space subdivision and automatic creation of a network for route computation. But currently it also represents only spaces as they are defined by the architectural layout of the building. The Land Administration Domain Model is currently the only available model to specify spaces on the basis of ownership and rights for use.In this paper we compare the principles of IndoorGML and LADM, investigate the approaches to define spaces and suggest options to the linking of the two types of spaces. We argue that LADM space subdivision on basis of properties and rights of use can be used to define to semantically and geometrically available and accessible spaces and therefore can enrich the IndoorGML concept.

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“…Correct navigation can be performed considering specific components, such as indoor localisation issues, accurate 3D models representing the space subdivision, 3D algorithms for path computation, guidance (Points of Interest), visualisation and correction of the path . Brown et al, (2013) completed a thorough study on indoor navigation, listing 15 generic requirements. In another paper, Isikdag et al (2013) define conceptual requirements for a building model for navigation purposes.…”

Section: Indoor Requirementsmentioning

confidence: 99%

“…The partition of space is another important aspect for indoor applications. Spaces must be categorized based on pre-defined space types depending on the user, scenario and the tasks to be performed (Brown et al, 2013), while the decomposition of indoor environments into smaller parts and their geo-coding supports better guidance and the generation of correct routes. Furthermore, spatial relationships between the elements (e.g.…”

Section: Indoor Requirementsmentioning

confidence: 99%

3d Network Analysis for Indoor Space Applications

Tsiliakou

Dimopoulou

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 KEY WORDS: indoor space, 3D modeling, network analysis, ArcScene, geoprocessing model, Network Analyst, CityEngine ABSTRACT:Indoor space differs from outdoor environments, since it is characterized by a higher level of structural complexity, geometry, as well as topological relations. Indoor space can be considered as the most important component in a building's conceptual modelling, on which applications such as indoor navigation, routing or analysis are performed. Therefore, the conceptual meaning of sub spaces or the activities taking place in physical building boundaries (e.g. walls), require the comprehension of the building's indoor hierarchical structure. The scope of this paper is to perform 3D network analysis in a building's interior and is structured as follows: In Section 1 the definition of indoor space is provided and indoor navigation requirements are analysed. Section 2 describes the processes of indoor space modeling, as well as routing applications. In Section 3, a case study is examined involving a 3D building model generated in CityEngine (exterior shell) and ArcScene (interior parts), in which the use of commercially available software tools (ArcGIS, ESRI), in terms of indoor routing and 3D network analysis, are explored. The fundamentals of performing 3D analysis with the ArcGIS Network Analyst extension were tested. Finally a geoprocessing model was presented, which was specifically designed to be used to interactively find the best route in ArcScene. The paper ends with discussion and concluding remarks on section 4.

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Modelling 3D Topographic Space Against Indoor Navigation Requirements

Cited by 47 publications

References 4 publications

Bim-Based Indoor Path Planning Considering Obstacles

Bim-Based Indoor Path Planning Considering Obstacles

Ladm and Indoorgml for Support of Indoor Space Identification

3d Network Analysis for Indoor Space Applications

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