Special Issue "3D Indoor Mapping and Modelling"

Special Issue Editors

Dr. Lucía Díaz-Vilariño
Website
Guest Editor
Circunvalación ao Campus Universitario, 36310 Vigo, Pontevedra, Spain
Interests: point cloud processing; 3D digital modelling; spatial analysis
Special Issues and Collections in MDPI journals
Dr. Abdoulaye Abou Diakité
Website
Guest Editor
Geospatial Research Innovation and Development (GRID) group, Faculty of Built Environment, University of New South Wales, Sydney, Australia
Interests: 3D indoor navigation; spatial analysis on BIM and GIS; 3D modelling; computational geometry
Mr. Shayan Nikoohemat
Website
Guest Editor
Department of Earth Observation Science (EOS), ITC Faculty, University of Twente, Enschede, The Netherlands
Interests: LiDAR processing; laser scanning; indoor modelling; indoor 3D reconstruction; cartography; SLAM; indoor navigation

Special Issue Information

Dear Colleagues,

In the last few years, there has been intense research activity towards the mapping and automated modelling of indoor environments. Updated and detailed indoor models are being increasingly demanded for a variety of applications, such as building management, indoor navigation, location-based services, and emergency responses. However, existing interior models are often not up-to-date, and consequently, they do not represent the as-is condition of the scene.

LiDAR scanning and photogrammetry have been revealed to be suitable techniques to collect data at a large scale, especially when dealing with portable and mobile acquisition systems. Nevertheless, point clouds, which constitute massive and unstructured data, need to be efficiently processed in a way that useful information for the applications they intend to serve is extracted. The complexity of indoor geometry, typically cluttered with people and furniture, necessitates (i) the further development of technologies for collecting high quality indoor data, especially in terms of completeness; (ii) the development of processing methods, efficient in time and cost, towards the automated modelling and interpretation of building indoors; and (iii) the improvement of unified storage and exchange possibilities of reconstructed 3D models, through the use of known BIM and GIS open standards (e.g. IFC, CityGML, IndoorGML, LADM).

This Special Issue aims at collecting new technologies, data collections and processing methodologies, and successful applications of indoor mapping and modelling. We welcome submissions which cover but are not limited to:

  • New sensing technologies for indoor mapping;
  • Geometric evaluation of indoor mapping systems;
  • Indoor data structures and models;
  • Scan-vs-BIM and building change detection;
  • Automated data analysis of 3D data (segmentation, classification, etc.);
  • Indoor reconstruction;
  • Scan-to-BIM standards (e.g., IFC);
  • Scan-to-GIS standards (e.g., CityGML, IndoorGML, LADM);
  • Multidimensional indoor representations (4D, 5D, etc.);
  • Indoor/outdoor seamless modelling and navigation;
  • Visualisation and simulation.

But GPS-denied indoors such as caves and tunnels are not in the scope of this issue.

Dr. Lucía Díaz-Vilariño
Dr. Abdoulaye Abou Diakité
Mr. Shayan Nikoohemat
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • point cloud processing
  • 3D indoor modelling
  • indoor localisation and mapping
  • navigation
  • spatial analysis
  • BIM

Published Papers (6 papers)

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Research

Open AccessArticle
3D Geometry-Based Indoor Network Extraction for Navigation Applications Using SFCGAL
ISPRS Int. J. Geo-Inf. 2020, 9(7), 417; https://doi.org/10.3390/ijgi9070417 - 29 Jun 2020
Abstract
This study is focused on indoor navigation network extraction for navigation applications based on available 3D building data and using SFCGAL library, e.g. simple features computational geometry algorithms library. In this study, special attention is given to 3D cadastre and BIM (building information [...] Read more.
This study is focused on indoor navigation network extraction for navigation applications based on available 3D building data and using SFCGAL library, e.g. simple features computational geometry algorithms library. In this study, special attention is given to 3D cadastre and BIM (building information modelling) datasets, which have been used as data sources for 3D geometric indoor modelling. SFCGAL 3D functions are used for the extraction of an indoor network, which has been modelled in the form of indoor connectivity graphs based on 3D geometries of indoor features. The extraction is performed by the integration of extract transform load (ETL) software and the spatial database to support multiple data sources and provide access to SFCGAL functions. With this integrated approach, the current lack of straightforward software support for complex 3D spatial analyses is addressed. Based on the developed methodology, we perform and discuss the extraction of an indoor navigation network from 3D cadastral and BIM data. The efficiency and performance of the network analyses were evaluated using the processing and query execution times. The results show that the proposed methodology for geometry-based navigation network extraction of buildings is efficient and can be used with various types of 3D geometric indoor data. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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Open AccessArticle
Indoor Positioning Using PnP Problem on Mobile Phone Images
ISPRS Int. J. Geo-Inf. 2020, 9(6), 368; https://doi.org/10.3390/ijgi9060368 - 02 Jun 2020
Abstract
As people grow accustomed to effortless outdoor navigation, there is a rising demand for similar possibilities indoors as well. Unfortunately, indoor localization, being one of the requirements for navigation, continues to be a problem without a clear solution. In this article, we are [...] Read more.
As people grow accustomed to effortless outdoor navigation, there is a rising demand for similar possibilities indoors as well. Unfortunately, indoor localization, being one of the requirements for navigation, continues to be a problem without a clear solution. In this article, we are proposing a method for an indoor positioning system using a single image. This is made possible using a small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with the SIFT (Scale Invariant Feature Transform) algorithm, we can look through the database and find an image that is the most similar to the image taken by a user. Such a pair of images is then used to find coordinates of a database of images using the PnP problem. Furthermore, projection and essential matrices are determined to calculate the user image localization—determining the position of the user in the indoor environment. The benefits of this approach lie in the single image being the only input from a user and the lack of requirements for new onsite infrastructure. Thus, our approach enables a more straightforward realization for building management. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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Open AccessArticle
Automatic Generation of High-Accuracy Stair Paths for Straight, Spiral, and Winder Stairs Using IFC-Based Models
ISPRS Int. J. Geo-Inf. 2020, 9(4), 215; https://doi.org/10.3390/ijgi9040215 - 31 Mar 2020
Abstract
The indoor space model is the foundation of most indoor location-based services (LBS). A complete indoor space model includes floor-level paths and non-level paths. The latter includes passages connecting different floors or elevations such as stairs, elevators, escalators, and ramps. Most related studies [...] Read more.
The indoor space model is the foundation of most indoor location-based services (LBS). A complete indoor space model includes floor-level paths and non-level paths. The latter includes passages connecting different floors or elevations such as stairs, elevators, escalators, and ramps. Most related studies have merely discussed the modeling and generation of floor-level paths, while those considering non-level paths usually simplify the formation and generation of non-level paths, especially stairs, which play an important role in emergency evacuation and response. Although the algorithm proposed by i-GIT approach, which considers both floor-level and non-level paths, can automatically generate paths of straight stairs, it is not applicable to the spiral stairs and winder stairs that are common in town houses and other public buildings. This study proposes a novel approach to generate high-accuracy stair paths that can support straight, spiral, and winder stairs. To implement and verify the proposed algorithm, 54 straight and spiral stairs provided by Autodesk Revit’s official website and three self-built winder stairs are used as test cases. The test results show that the algorithm can successfully produce the stair paths of most test cases (49/50), which comprehensively extends the applicability of the proposed algorithm. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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Open AccessArticle
DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes
ISPRS Int. J. Geo-Inf. 2020, 9(4), 202; https://doi.org/10.3390/ijgi9040202 - 27 Mar 2020
Abstract
Most Simultaneous Localization and Mapping (SLAM) methods assume that environments are static. Such a strong assumption limits the application of most visual SLAM systems. The dynamic objects will cause many wrong data associations during the SLAM process. To address this problem, a novel [...] Read more.
Most Simultaneous Localization and Mapping (SLAM) methods assume that environments are static. Such a strong assumption limits the application of most visual SLAM systems. The dynamic objects will cause many wrong data associations during the SLAM process. To address this problem, a novel visual SLAM method that follows the pipeline of feature-based methods called DM-SLAM is proposed in this paper. DM-SLAM combines an instance segmentation network with optical flow information to improve the location accuracy in dynamic environments, which supports monocular, stereo, and RGB-D sensors. It consists of four modules: semantic segmentation, ego-motion estimation, dynamic point detection and a feature-based SLAM framework. The semantic segmentation module obtains pixel-wise segmentation results of potentially dynamic objects, and the ego-motion estimation module calculates the initial pose. In the third module, two different strategies are presented to detect dynamic feature points for RGB-D/stereo and monocular cases. In the first case, the feature points with depth information are reprojected to the current frame. The reprojection offset vectors are used to distinguish the dynamic points. In the other case, we utilize the epipolar constraint to accomplish this task. Furthermore, the static feature points left are fed into the fourth module. The experimental results on the public TUM and KITTI datasets demonstrate that DM-SLAM outperforms the standard visual SLAM baselines in terms of accuracy in highly dynamic environments. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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Open AccessArticle
Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities
ISPRS Int. J. Geo-Inf. 2020, 9(2), 66; https://doi.org/10.3390/ijgi9020066 - 21 Jan 2020
Cited by 1
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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Open AccessArticle
Accumulative Errors Optimization for Visual Odometry of ORB-SLAM2 Based on RGB-D Cameras
ISPRS Int. J. Geo-Inf. 2019, 8(12), 581; https://doi.org/10.3390/ijgi8120581 - 11 Dec 2019
Cited by 2
Abstract
Oriented feature from the accelerated segment test (oFAST) and rotated binary robust independent elementary features (rBRIEF) SLAM2 (ORB-SLAM2) represent a recognized complete visual simultaneous location and mapping (SLAM) framework with visual odometry as one of its core components. Given the accumulated error problem [...] Read more.
Oriented feature from the accelerated segment test (oFAST) and rotated binary robust independent elementary features (rBRIEF) SLAM2 (ORB-SLAM2) represent a recognized complete visual simultaneous location and mapping (SLAM) framework with visual odometry as one of its core components. Given the accumulated error problem with RGB-Depth ORB-SLAM2 visual odometry, which causes a loss of camera tracking and trajectory drift, we created and implemented an improved visual odometry method to optimize the cumulative error. First, this paper proposes an adaptive threshold oFAST algorithm to extract feature points from images and rBRIEF is used to describe the feature points. Then, the fast library for approximate nearest neighbors strategy is used for image rough matching, the results of which are optimized by progressive sample consensus. The image matching precision is further improved by using an epipolar line constraint based on the essential matrix. Finally, the efficient Perspective-n-Point method is used to estimate the camera pose and a least-squares optimization problem is constructed to adjust the estimated value to obtain the final camera pose. The experimental results show that the proposed method has better robustness, higher image matching accuracy and more accurate determination of the camera motion trajectory. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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