Special Issue "3D Indoor Mapping and Modelling"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 October 2020).

Special Issue Editors

Dr. Lucía Díaz-Vilariño
E-Mail Website
Guest Editor
Applied Geotechnologies Research Group, University of Vigo, Vigo, Spain
Interests: point cloud processing; 3D digital modeling; spatial analysis
Special Issues and Collections in MDPI journals
Dr. Abdoulaye Abou Diakité
E-Mail 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
Dr. Shayan Nikoohemat
E-Mail Website
Guest Editor
Department of Earth Observation Science, University of Twente, 7522 NB Enschede, The Netherlands
Interests: spatial analysis; mapping; geoinformation
Special Issues and Collections in MDPI journals

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 1400 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 (10 papers)

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Research

Article
Pairwise Coarse Registration of Indoor Point Clouds Using 2D Line Features
ISPRS Int. J. Geo-Inf. 2021, 10(1), 26; https://doi.org/10.3390/ijgi10010026 - 12 Jan 2021
Viewed by 526
Abstract
Registration is essential for terrestrial LiDAR (light detection and ranging) scanning point clouds. The registration of indoor point clouds is especially challenging due to the occlusion and self-similarity of indoor structures. This paper proposes a 4 degrees of freedom (4DOF) coarse registration method [...] Read more.
Registration is essential for terrestrial LiDAR (light detection and ranging) scanning point clouds. The registration of indoor point clouds is especially challenging due to the occlusion and self-similarity of indoor structures. This paper proposes a 4 degrees of freedom (4DOF) coarse registration method that fully takes advantage of the knowledge that the equipment is levelled or the inclination compensated for by a tilt sensor in data acquisition. The method decomposes the 4DOF registration problem into two parts: (1) horizontal alignment using ortho-projected images and (2) vertical alignment. The ortho-projected images are generated using points between the floor and ceiling, and the horizontal alignment is achieved by the matching of the source and target ortho-projected images using the 2D line features detected from them. The vertical alignment is achieved by making the height of the floor and ceiling in the source and target points equivalent. Two datasets, one with five stations and the other with 20 stations, were used to evaluate the performance of the proposed method. The experimental results showed that the proposed method achieved 80% and 63% successful registration rates (SRRs) in a simple scene and a challenging scene, respectively. The SRR in the simple scene is only lower than that of the keypoint-based four-point congruent set (K4PCS) method. The SRR in the challenging scene is better than all five comparison methods. Even though the proposed method still has some limitations, the proposed method provides an alternative to solve the indoor point cloud registration problem. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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Article
Indoor Positioning Method Using WiFi RTT Based on LOS Identification and Range Calibration
ISPRS Int. J. Geo-Inf. 2020, 9(11), 627; https://doi.org/10.3390/ijgi9110627 - 26 Oct 2020
Cited by 2 | Viewed by 928
Abstract
WiFi-based indoor positioning methods have attracted extensive attention due to the wide installation of WiFi access points (APs). Recently, the WiFi standard was modified and introduced into a new two-way approach based on round trip time (RTT) measurement, which brings some changes for [...] Read more.
WiFi-based indoor positioning methods have attracted extensive attention due to the wide installation of WiFi access points (APs). Recently, the WiFi standard was modified and introduced into a new two-way approach based on round trip time (RTT) measurement, which brings some changes for indoor positioning based on WiFi. In this work, we propose a WiFi RTT positioning method based on line of sight (LOS) identification and range calibration. Given the complexity of the indoor environment, we design a non-line of sight (NLOS) and LOS identification algorithm based on scenario recognition. The positioning scenario is recognized to assist NLOS and LOS distances identification, and gaussian process regression (GPR) is utilized to construct the scenario recognition model. Meanwhile, the calibration model for LOS distance is presented to correct the measuring distance and the scenario information is utilized to constrain the estimated position. When there is a positioning request, the positioning scenario is identified with the scenario recognition model, and LOS measuring distance is obtained based on the recognized scenario. The LOS range measurements are first calibrated and then utilized to estimate the position of the smartphone. Finally, the positioning scenario is used to constrain the estimation location to avoid it beyond the scenario. The experimental results show that the positioning effect of the proposed method is far better than that of the Least Squares (LS) algorithm, achieving a mean error (ME) of 0.862 m and root-mean-square error (RMSE) of 0.989 m. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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Article
Drift Invariant Metric Quality Control of Construction Sites Using BIM and Point Cloud Data
ISPRS Int. J. Geo-Inf. 2020, 9(9), 545; https://doi.org/10.3390/ijgi9090545 - 14 Sep 2020
Viewed by 846
Abstract
Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by [...] Read more.
Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by high failure costs which are often caused by erroneously constructed structural objects. With the upcoming use of periodic remote sensing during the different phases of the building process, new possibilities arise to advance from a selective quality analysis to an in-depth assessment of the full construction site. In this work, a novel methodology is presented to rapidly evaluate a large number of built objects on a construction site. Given a point cloud and a set of as-design BIM elements, our method evaluates the deviations between both datasets and computes the positioning errors of each object. Unlike the current state of the art, our method computes the error vectors regardless of drift, noise, clutter and (geo)referencing errors, leading to a better detection rate. The main contributions are the efficient matching of both datasets, the drift invariant metric evaluation and the intuitive visualisation of the results. The proposed analysis facilitates the identification of construction errors early on in the process, hence significantly lowering the failure costs. The application is embedded in native BIM software and visualises the objects by a simple color code, providing an intuitive indicator for the positioning accuracy of the built objects. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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Article
A Real-Time Infrared Stereo Matching Algorithm for RGB-D Cameras’ Indoor 3D Perception
ISPRS Int. J. Geo-Inf. 2020, 9(8), 472; https://doi.org/10.3390/ijgi9080472 - 28 Jul 2020
Viewed by 796
Abstract
Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has [...] Read more.
Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has a small detection range, short measurement distance and low depth map resolution, which severely restrict its usage scenarios and service life. For these problems, on the basis of the existing research, a novel infrared stereo matching algorithm that combines the idea of the semi-global method and sliding window is proposed in this paper. First, the R200 is calibrated. Then, through Gaussian filtering, the mutual information and correlation between the left and right stereo infrared images are enhanced. According to mutual information, the dynamic threshold selection in matching is realized, so the adaptability to different scenes is improved. Meanwhile, the robustness of the algorithm is improved by the Sobel operators in the cost calculation of the energy function. In addition, the accuracy and quality of disparity values are improved through a uniqueness test and sub-pixel interpolation. Finally, the BundleFusion algorithm is used to reconstruct indoor 3D surface models in different scenarios, which proved the effectiveness and superiority of the stereo matching algorithm proposed in this paper. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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Article
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
Cited by 1 | Viewed by 923
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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Article
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
Cited by 2 | Viewed by 842
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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Article
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
Cited by 1 | Viewed by 842
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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Article
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
Cited by 4 | Viewed by 1106
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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Article
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 5 | Viewed by 862
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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Article
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 3 | Viewed by 1069
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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