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Point Cloud Data Acquisition, Analysis, and Management for Construction Industry

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 18269

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Special Issue Editors

Prof. Joon Heo

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Guest Editor

Civil and Environmental Engineering, Yonsei University, Seoul, Korea
Interests: spatial data science; lidar data processing; as-built BIM; big spatial data management; construction applications of mapping and data science

Prof. Changjae Kim

E-Mail Website
Guest Editor

Department of Civil and Environmental Engineering, Myongji University, 116 Myongji-ro, Cheoin-gu, Yongin, Gyeonggi-do 17058, Korea
Interests: LiDAR; photogrammetry; sensor integration; SLAM; 3D modeling; indoor positioning

Prof. Minkoo Kim

E-Mail Website
Guest Editor

Department of Architectural Engineering, Chungbuk National University, Cheongju-si, Korea
Interests: sensing-based construction automation; geometric quality inspection; 3D point cloud data acquisition; scan planning; building information modeling (BIM)

Special Issue Information

Dear Colleagues,

The Journal Remote Sensing (ISSN 2072-4292, Impact Factor 4.118) is launching a Special Issue, entitled, “Point Cloud Data Acquisition, Analysis, and Management for the Construction Industry”.

Point cloud data are becoming more and more popular in the construction industry, which is undergoing a digital transformation in order to overcome a long-standing low productivity issue in comparison with other major industry sectors. Infusion of technology and advanced automation is considered one of the solutions to boost sector productivity, where point cloud data are often used for recording and capturing the shape and appearance of a construction site, and new data are even opening up new opportunities to be integrated with emerging digital technologies.

Laser scanning and other technology to extract point cloud have contributed to improving construction productivity, as well as quality, in different phases of the construction life cycle. Just to name a few, in the planning and design phase, point cloud data are used for characterizing and understanding project sites. In the fabrication phase, they are leveraged to check the geometric quality of prefabricated components. In the construction phase, progress reporting and verification can be conducted using point cloud. In the operation and maintenance phase, point cloud can be used for 3D modeling and as-built BIM of existing facility, performance analysis, early fault detection, facility renovation and retrofit, and so on. From a technical perspective, all the above applications are composed of point cloud data acquisition methods, point cloud data processing and analyses, and management and/or visualization of particularly ‘big’ point cloud data. All these things considered, the Special Issue was entitled “Point Cloud Data Acquisition, Analysis, and Management for the Construction Industry”.

Prospective authors are invited to contribute to this Special Issue by submitting an original manuscript of their latest research related to point cloud data for construction industry. Original and innovative contributions may be from, but not limited to:

Development of point cloud data acquisition systems;
Calibration of integrated sensors, such as lidar, image, and IMU;
SLAM solutions customized for construction environments;
Robot navigation system for construction environments;
3D modeling for as-built BIM;
Geometric quality inspection of pre- and post-fabrication;
Construction object segmentation and recognition;
Change detection of construction sites;
Defect detection and quantification;
QA/QC for construction site;
Progress tracking and conflict monitoring;
Construction labor monitoring;
‘Big’ point cloud data management in construction cases;
‘Big’ point cloud data visualization in construction cases.

Prof. Joon Heo
Prof. Changjae Kim
Prof. Minkoo Kim
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 submissions that pass pre-check are 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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 data
SLAM
Robot navigation
3D geometric modeling
As-built BIM
Progress tracking
Construction object recognition
Change detection
QA/QC
Labor monitoring
Point cloud data management

Published Papers (5 papers)

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Research

22 pages, 26462 KiB

Open AccessArticle

Plane-Based Robust Registration of a Building Scan with Its BIM

by Noaman Akbar Sheik, Greet Deruyter and Peter Veelaert

Remote Sens. 2022, 14(9), 1979; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14091979 - 20 Apr 2022

Cited by 14 | Viewed by 2183

Abstract

The registration of as-built and as-planned building models is a pre-requisite in automated construction progress monitoring. Due to the numerous challenges associated with the registration process, it is still performed manually. This research study proposes an automated registration method that aligns the as-built point cloud of a building to its as-planned model using its planar features. The proposed method extracts and processes all the plane segments from both the as-built and the as-planned models, then—for both models—groups parallel plane segments into clusters and subsequently determines the directions of these clusters to eventually determine a range of possible rotation matrices. These rotation matrices are then evaluated through a computational framework based on a postulation concerning the matching of plane segments from both models. This framework measures the correspondence between the plane segments through a matching cost algorithm, thus identifying matching plane segments, which ultimately leads to the determination of the transformation parameters to correctly register the as-built point cloud to its as-planned model. The proposed method was validated by applying it to a range of different datasets. The results proved the robustness of the method both in terms of accuracy and efficiency. In addition, the method also proved its correct support for the registration of buildings under construction, which are inherently incomplete, bringing research a step closer to practical and effective construction progress monitoring. Full article

(This article belongs to the Special Issue Point Cloud Data Acquisition, Analysis, and Management for Construction Industry)

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21 pages, 9348 KiB

Open AccessArticle

Automated BIM Reconstruction of Full-Scale Complex Tubular Engineering Structures Using Terrestrial Laser Scanning

by Jiepeng Liu, Lihua Fu, Guozhong Cheng, Dongsheng Li, Jing Zhou, Na Cui and Y. Frank Chen

Remote Sens. 2022, 14(7), 1659; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071659 - 30 Mar 2022

Cited by 6 | Viewed by 2268

Abstract

Due to the accumulation of manufacturing errors of components and construction errors, there are always deviations between an as-built complex tubular engineering structure (CTES) and its as-designed model. As terrestrial laser scanning (TLS) provides accurate point cloud data (PCD) for scanned objects, it can be used in the building information modeling (BIM) reconstruction of as-built CTESs for life cycle management. However, few studies have focused on the BIM reconstruction of a full-scale CTES from missing and noisy PCD. To this end, this study proposes an automated BIM reconstruction method based on the TLS for a full-scale CTES. In particular, a novel algorithm is proposed to extract the central axis of a tubular structure. An extended axis searching algorithm is applied to segment each component PCD. A slice-based method is used to estimate the geometric parameters of curved tubes. The proposed method is validated through a full-scale CTES, where the maximum error is 0.92 mm. Full article

(This article belongs to the Special Issue Point Cloud Data Acquisition, Analysis, and Management for Construction Industry)

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17 pages, 6915 KiB

Open AccessArticle

Laser Scanning Based Surface Flatness Measurement Using Flat Mirrors for Enhancing Scan Coverage Range

by Fangxin Li, Heng Li, Min-Koo Kim and King-Chi Lo

Remote Sens. 2021, 13(4), 714; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040714 - 15 Feb 2021

Cited by 11 | Viewed by 3246

Abstract

Surface flatness is an important indicator for the quality assessment of concrete surfaces during and after slab construction in the construction industry. Thanks to its speed and accuracy, terrestrial laser scanning (TLS) has been popularly used for surface flatness inspection of concrete slabs. However, the current TLS based approach for surface flatness inspection has two primary limitations associated with scan range and occluded area. First, the areas far away from the TLS normally suffer from inaccurate measurement caused by low scan density and high incident angle of laser beams. Second, physical barriers such as interior walls cause occluded areas where the TLS is not able to scan for surface flatness inspection. To address these limitations, this study presents a new method that employs flat mirrors to increase the measurement range with acceptable measurement accuracy and make possible the scanning of occluded areas even when the TLS is out of sight. To validate the proposed method, experiments on two laboratory-scale specimens are conducted, and the results show that the proposed approach can enlarge the scan range from 5 m to 10 m. In addition, the proposed method is able to address the occlusion problem of the previous methods by changing the laser beam direction. Based on these results, it is expected that the proposed technique has the potential for accurate and efficient surface flatness inspection in the construction industry. Full article

(This article belongs to the Special Issue Point Cloud Data Acquisition, Analysis, and Management for Construction Industry)

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Graphical abstract

29 pages, 8234 KiB

Open AccessArticle

BIM-Based Registration and Localization of 3D Point Clouds of Indoor Scenes Using Geometric Features for Augmented Reality

by Bilawal Mahmood, SangUk Han and Dong-Eun Lee

Remote Sens. 2020, 12(14), 2302; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142302 - 17 Jul 2020

Cited by 38 | Viewed by 5505

Abstract

Augmented reality can improve construction and facility management by visualizing an as-planned model on its corresponding surface for fast, easy, and correct information retrieval. This requires the localization registration of an as-built model in an as-planned model. However, the localization and registration of indoor environments fail, owing to self-similarity in an indoor environment, relatively large as-planned models, and the presence of additional unplanned objects. Therefore, this paper proposes a computer vision-based method to (1) homogenize indoor as-planned and as-built models, (2) reduce the search space of model matching, and (3) localize the structure (e.g., room) for registration of the scanned area in its as-planned model. This method extracts a representative horizontal cross section from the as-built and as-planned point clouds to make these models similar, restricts unnecessary transformation to reduce the search space, and corresponds the line features for the estimation of the registration transformation matrix. The performance of this method, in terms of registration accuracy, is evaluated on as-built point clouds of rooms and a hallway on a building floor. A rotational error of 0.005 rad and a translational error of 0.088 m are observed in the experiments. Hence, the geometric feature described on a representative cross section with transformation restrictions can be a computationally cost-effective solution for indoor localization and registration. Full article

(This article belongs to the Special Issue Point Cloud Data Acquisition, Analysis, and Management for Construction Industry)

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Graphical abstract

17 pages, 5442 KiB

Open AccessArticle

Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

by Yaxin Li, Wenbin Li, Walid Darwish, Shengjun Tang, Yuling Hu and Wu Chen

Remote Sens. 2020, 12(2), 320; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12020320 - 18 Jan 2020

Cited by 9 | Viewed by 3585

Abstract

Plane fitting is a fundamental operation for point cloud data processing. Most existing methods for point cloud plane fitting have been developed based on high-quality Lidar data giving equal weight to the point cloud data. In recent years, using low-quality RGB-Depth (RGB-D) sensors to generate 3D models has attracted much attention. However, with low-quality point cloud data, equal weight plane fitting methods are not optimal as the range errors of RGB-D sensors are distance-related. In this paper, we developed an accurate plane fitting method for a structured light (SL)-based RGB-D sensor. First, we derived an error model of a point cloud dataset from the SL-based RGB-D sensor through error propagation from the raw measurement to the point coordinates. A new cost function based on minimizing the radial distances with the derived rigorous error model was then proposed for the random sample consensus (RANSAC)-based plane fitting method. The experimental results demonstrated that our method is robust and practical for different operating ranges and different working conditions. In the experiments, for the operating ranges from 1.23 meters to 4.31 meters, the mean plane angle errors were about one degree, and the mean plane distance errors were less than six centimeters. When the dataset is of a large-depth-measurement scale, the proposed method can significantly improve the plane fitting accuracy, with a plane angle error of 0.5 degrees and a mean distance error of 4.7 cm, compared to 3.8 degrees and 16.8 cm, respectively, from the conventional un-weighted RANSAC method. The experimental results also demonstrate that the proposed method is applicable for different types of SL-based RGB-D sensor. The rigorous error model of the SL-based RGB-D sensor is essential for many applications such as in outlier detection and data authorization. Meanwhile, the precise plane fitting method developed in our research will benefit algorithms based on high-accuracy plane features such as depth calibration, 3D feature-based simultaneous localization and mapping (SLAM), and the generation of indoor building information models (BIMs). Full article

(This article belongs to the Special Issue Point Cloud Data Acquisition, Analysis, and Management for Construction Industry)

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