scholarly journals A Simplified CityGML-Based 3D Indoor Space Model for Indoor Applications

2020 ◽  
Vol 10 (20) ◽  
pp. 7218
Author(s):  
Qun Sun ◽  
Xiaoguang Zhou ◽  
Dongyang Hou
Keyword(s):  
Semantic Information ◽  
Indoor Navigation ◽  
Geometric Information ◽  
Indoor Space ◽  
Emergency Rescue ◽  
Interior Spaces ◽  
Topological Relationships ◽  
Building Components ◽  
Relationship Of ◽  
Indoor Spaces

With the continuous development of indoor positioning technology, various indoor applications, such as indoor navigation and emergency rescue, have gradually received widespread attention. Indoor navigation and emergency rescue require access to a variety of indoor space information, such as accurate geometric information, rich semantic information and indoor spatial adjacency information; hence, a suitable 3D indoor model is needed. However, the available models, such as BIM and CityGML, mainly represent geometric and semantic information of indoor spaces, and rarely describe the topological adjacency relationship of interior spaces. To address the requirements of indoor navigation and emergency rescue, a simplified 3D indoor model is proposed in this research. The building components and indoor functional spaces of buildings are described in a simplified way. The geometric and semantic information are described based on CityGML, and the topological relationships of indoor adjacent spaces are represented by CityGML XLinks. While describing the indoor level of detail (LOD) of buildings in detail, the model simplifies building components and indoor spaces, which can preserve the characteristics of indoor spaces to the maximum extent and serve as a basis for indoor applications.

scholarly journals BIM-BASED INDOOR PATH PLANNING CONSIDERING OBSTACLES

2017 ◽  
Vol IV-2/W4 ◽  
pp. 417-423 ◽  
Author(s):  
M. Xu ◽  
S. Wei ◽  
S. Zlatanova ◽  
R. Zhang
Keyword(s):  
Path Planning ◽  
Semantic Information ◽  
Indoor Navigation ◽  
Triangular Prism ◽  
Building Structures ◽  
Indoor Space ◽  
Space Subdivision ◽  
3D Space ◽  
Building Components ◽  
2D And 3D

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.

scholarly journals A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Jin Zhu ◽  
Dayu Cheng ◽  
Weiwei Zhang ◽  
Ci Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Semantic Information ◽  
Edit Distance ◽  
Similarity Measures ◽  
Indoor Positioning ◽  
Synthetic Dataset ◽  
Shopping Mall ◽  
Indoor Space ◽  
Trajectory Similarity ◽  
Indoor Spaces

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

scholarly journals Development of an Omnidirectional-Image-Based Data Model through Extending the IndoorGML Concept to an Indoor Patrol Service

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Hyo-jin Jung ◽  
Jiyeong Lee
Keyword(s):  
Data Model ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Indoor Location ◽  
Indoor Space ◽  
Omnidirectional Image ◽  
Space Data ◽  
Omnidirectional Images ◽  
Indoor Spaces ◽  
Topological Data

Different indoor representation methods have been studied for their ability to provide indoor location-based services (LBS). Among them, omnidirectional imaging is one of the most typical and simple methods for representing an indoor space. However, a georeferenced omnidirectional image cannot be used for simple attribute searches, spatial queries, and spatial awareness analyses. To perform these functions, topological data are needed to define the features of and spatial relationships among spatial objects including indoor spaces as well as facilities like CCTV cameras considered in patrol service applications. Therefore, this study proposes an indoor space application data model for an indoor patrol service that can implement functions suited to linking indoor space data and service objects. In order to do this, the study presents a method for linking data between omnidirectional images representing indoor spaces and topological data on indoor spaces based on the concept of IndoorGML. Also, we conduct an experimental implementation of the integrated 3D indoor navigation model for patrol service using GIS data. Based on the results, we evaluate the benefits of using such a 3D data fusion method that integrates omnidirectional images with vector-based topological data models based on IndoorGML for providing indoor LBS in built environments.

scholarly journals INDOOR SPACE ROUTING GRAPHS: VISIBILITY, ENCODING, ENCRYPTION AND ATTENUATION

2018 ◽  
Vol XLII-4 ◽  
pp. 579-585
Author(s):  
G. Sithole
Keyword(s):  
Path Planning ◽  
Indoor Environment ◽  
Indoor Navigation ◽  
Indoor Space ◽  
The Third ◽  
Future Paper ◽  
Spatial Geometry ◽  
Novel Method ◽  
Third Dimension ◽  
Indoor Spaces

<p><strong>Abstract.</strong> The conventional approach to path planning for indoor navigation is to infer routes from a subdivided floor map of the indoor space. The floor map describes the spatial geometry of the space. Contained in this floor map are logical units called subspaces. For the purpose of path planning the possible routes between the subspaces have to be modelled. Typical these models employing a graph structures, or skeletons, in which the interconnected subspaces (e.g., rooms, corridors, etc.) are represented as linked nodes, i.e. a graph.</p><p>This paper presents a novel method for creating generalised graphs of indoor spaces that doesn’t require the subdivision of indoor space. The method creates the generalised graph by gradually simplifying/in-setting the floor map until a graph is obtained, a process described here as chained deflation. The resulting generalised graph allows for more flexible and natural paths to be determined within the indoor environment. Importantly the method allows the indoor space to be encoded and encrypted and supplied to users in a way that emulates the use of physical keys in the real world. Another important novelty of the method is that the space described by the graph is adaptable. The space described by the graph can be deflated or inflated according to the needs of the path planning. Finally, the proposed method can be readily generalised to the third dimension.</p><p>The concept and logic of the method are explained. A full implementation of the method will be discussed in a future paper.</p>

scholarly journals A SEMANTIC MODEL TO DEFINE INDOOR SPACE IN CONTEXT OF EMERGENCY EVACUATION

2016 ◽  
Vol XLI-B4 ◽  
pp. 313-318 ◽  
Author(s):  
Nishith Maheshwari ◽  
K. S. Rajan
Keyword(s):  
Data Model ◽  
Semantic Information ◽  
Emergency Evacuation ◽  
Facility Management ◽  
Semantic Model ◽  
Real World Data ◽  
Geometric Information ◽  
Indoor Space ◽  
Syntactic Features ◽  
And Function

There have been various ways in which the indoor space of a building has been defined. In most of the cases the models have specific purpose on which they focus such as facility management, visualisation or navigation. The focus of our work is to define semantics of a model which can incorporate different aspects of the space within a building without losing any information provided by the data model. In this paper we have suggested a model which defines indoor space in terms of semantic and syntactic features. Each feature belongs to a particular class and based on the class, has a set of properties associated with it. The purpose is to capture properties like geometry, topology and semantic information like name, function and capacity of the space from a real world data model. The features which define the space are determined using the geometric information and the classes are assigned based on the relationships like connectivity, openings and function of the space. The ontology of the classes of the feature set defined will be discussed in the paper.

scholarly journals A SEMANTIC MODEL TO DEFINE INDOOR SPACE IN CONTEXT OF EMERGENCY EVACUATION

2016 ◽  
Vol XLI-B4 ◽  
pp. 313-318
Author(s):  
Nishith Maheshwari ◽  
K. S. Rajan
Keyword(s):  
Data Model ◽  
Semantic Information ◽  
Emergency Evacuation ◽  
Facility Management ◽  
Semantic Model ◽  
Real World Data ◽  
Geometric Information ◽  
Indoor Space ◽  
Syntactic Features ◽  
And Function

There have been various ways in which the indoor space of a building has been defined. In most of the cases the models have specific purpose on which they focus such as facility management, visualisation or navigation. The focus of our work is to define semantics of a model which can incorporate different aspects of the space within a building without losing any information provided by the data model. In this paper we have suggested a model which defines indoor space in terms of semantic and syntactic features. Each feature belongs to a particular class and based on the class, has a set of properties associated with it. The purpose is to capture properties like geometry, topology and semantic information like name, function and capacity of the space from a real world data model. The features which define the space are determined using the geometric information and the classes are assigned based on the relationships like connectivity, openings and function of the space. The ontology of the classes of the feature set defined will be discussed in the paper.

scholarly journals Development of an Indoor Space Semantic Model and Its Implementation as an IndoorGML Extension

2019 ◽  
Vol 8 (8) ◽  
pp. 333 ◽  
Author(s):  
Nishith Maheshwari ◽  
Srishti Srivastava ◽  
Krishnan Sundara Rajan
Keyword(s):  
Semantic Information ◽  
Classification Model ◽  
Semantic Model ◽  
Data Format ◽  
Building Evacuation ◽  
Indoor Space ◽  
Physical Representation ◽  
History Of ◽  
Space Semantic ◽  
Indoor Spaces

Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to note that many applications look for the semantic information to be made available. This paper proposes a space classification model leading to an ontology for indoor spaces that accounts for both the semantic and geometric characteristics of the spaces. Further, a Space semantic model is defined, based on this ontology, which can then be used appropriately in multiple applications. To demonstrate the utility of the model, we also present an extension to the IndoorGML data standard with a set of proposed classes that can help capture both the syntactic and semantic components of the model. It is expected that these proposed classes can be appropriately harnessed for use in diverse applications ranging from indoor data visualization to more user customised building evacuation path planning with a semantic overtone.

scholarly journals MULTI-LEVEL INDOOR PATH PLANNING METHOD

2015 ◽  
Vol XL-4/W5 ◽  
pp. 19-23 ◽  
Author(s):  
Q. Xiong ◽  
Q. Zhu ◽  
S. Zlatanova ◽  
Z. Du ◽  
Y. Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
Semantic Information ◽  
Fundamental Problem ◽  
Relevant Literature ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
3D Cad ◽  
Building Models ◽  
Complex Building ◽  
Building Components

Indoor navigation is increasingly widespread in complex indoor environments, and indoor path planning is the most important part of indoor navigation. Path planning generally refers to finding the most suitable path connecting two locations, while avoiding collision with obstacles. However, it is a fundamental problem, especially for 3D complex building model. A common way to solve the issue in some applications has been approached in a number of relevant literature, which primarily operates on 2D drawings or building layouts, possibly with few attached attributes for obstacles. Although several digital building models in the format of 3D CAD have been used for path planning, they usually contain only geometric information while losing abundant semantic information of building components (e.g. types and attributes of building components and their simple relationships). Therefore, it becomes important to develop a reliable method that can enhance application of path planning by combining both geometric and semantic information of building components. This paper introduces a method that support 3D indoor path planning with semantic information.

scholarly journals Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory

2018 ◽  
Vol 10 (11) ◽  
pp. 1815 ◽  
Author(s):  
Ahmed Elseicy ◽  
Shayan Nikoohemat ◽  
Michael Peter ◽  
Sander Oude Elberink
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Laser Scanning ◽  
Semantic Information ◽  
State Of The Art ◽  
Laser Scanner ◽  
Indoor Space ◽  
Laser Scanners ◽  
Real World Datasets ◽  
Indoor Spaces

State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases.

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

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