Towards the Integration of Indoorgml and Indoorlocationgml for Indoor Applications

Liu, L.; Zlatanova, Sisi; Zhu, Qing; Li, K.

doi:10.5194/isprs-annals-iv-2-w4-343-2017

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…As complementary to one another, Kim et al [159] generated IndoorGML data from CityGML LoD4 and offered external references from IndoorGML to an object in CityGML LoD4. Liu et al [160] integrated IndoorGML with IndoorLocationGML, where IndoorGML offers the subdivision of indoor spaces, while the location semantics in IndoorLocationGML can be introduced. To address the limitations of the standardized level of detail (LoD), Tang et al [161] extended an indoor LoD specification using both IFC and IndoorGML to achieve a full LoD specification for 3D building models.…”

Section: Topological Modelingmentioning

confidence: 99%

A Review of Techniques for 3D Reconstruction of Indoor Environments

Zhang

Yang

Zhou

et al. 2020

IJGI

114

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Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand.

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Section: Topological Modelingmentioning

confidence: 99%

A Review of Techniques for 3D Reconstruction of Indoor Environments

Zhang

Yang

Zhou

et al. 2020

IJGI

114

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“…Two kinds of models can be created: (1) generalized indoor models for broad classes of users; (2) models for specific purposes. Many studies have performed modeling based on indoor space standards; these models include CityGML, IndoorGML, and the spatial information model defined in ISO19107 [5,8,10,[13][14][15][16][17].…”

Section: Related Workmentioning

confidence: 99%

“…An indoor navigation module contains all the contents of the core module and adds a navigable space feature that includes a general space for staying and a transfer space for traveling. The IndoorGML extension module can be continuously added depending on the application, therefore extensions for various purposes have been proposed [8,10,11,16,17]. This study developed an IndoorGML extension to support indoor navigation for people with mobility disabilities in indoor environments.…”

Section: Indoorgml Core and Navigation Modulementioning

confidence: 99%

Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities

Park

Kim

2020

IJGI

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The increasing complexity of modern buildings has challenged the mobility of people with disabilities (PWD) in the indoor environment. To help overcome this problem, this paper proposes a data model that can be easily applied to indoor spatial information services for people with disabilities. In the proposed model, features are defined based on relevant regulations that stipulate significant mobility factors for people with disabilities. To validate the model’s capability to describe the indoor spaces in terms that are relevant to people with mobility disabilities, the model was used to generate data in a path planning application, considering two different cases in a shopping mall. The application confirmed that routes for people with mobility disabilities are significantly different from those of ordinary pedestrians, in a way that reflects features and attributes defined in the proposed data model. The latter can be inserted as an IndoorGML extension, and is thus expected to facilitate relevant data generation for the design of various services for people with disabilities.

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“…Zhu et al introduced a Chinese national standard of indoor navigation model: Indoor Multi-Dimensional Location Geography Markup Language (IndoorLocationGML), which focused on the descriptions of indoor locations and provided a framework of indoor location services [21]. Liu et al took an initiative in attempting to integrate IndoorGML and IndoorLocationGML and presented the possibility to combine two indoor-related standards for indoor applications [22]. Alattas et al presented a combined LADM-IndoorGML model, which combined use of the indoor IndoorGML and LADM models to support indoor navigation using access rights to spaces [23,24].…”

Section: Related Workmentioning

confidence: 99%

Three-Dimensional Indoor Fire Evacuation Routing

Zhou

Pang

Chen

et al. 2020

IJGI

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Traditional indoor navigation algorithms generally only consider the geometrical information of indoor space. However, the environmental information and semantic parameters of a fire are also important for evacuation routing in the case of a fire. It is difficult for traditional indoor navigation algorithms to dynamically find an indoor path when a fire develops. To address this problem, we developed a multi-semantic constrained three-dimensional (3D) indoor fire evacuation routing method that considers multi-dimensional indoor fire scene-related semantics, such as path accessibility, path recognition degree, and fire parameters. Our method enhances the navigation semantics of indoor space by extending the fire-related components of indoor model based on IndoorGML and integrating location semantics of IndoorLocationGML. We also propose quantifiable indoor fire-oriented routing semantics and establish a navigation cost function that evaluates semantic changes during a fire. We designed an indoor routing algorithm with multiple semantic constraints based on the A* algorithm. The indoor routing results were analyzed and compared in simulation experiments. The experimental results showed that the proposed model can remove unusable nodes and edges from the obtained navigation path and provides a safer and more effective evacuation route than traditional algorithms.

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Towards the Integration of Indoorgml and Indoorlocationgml for Indoor Applications

Cited by 5 publications

References 16 publications

A Review of Techniques for 3D Reconstruction of Indoor Environments

A Review of Techniques for 3D Reconstruction of Indoor Environments

Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities

Three-Dimensional Indoor Fire Evacuation Routing

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