Change detection for indoor construction progress monitoring based on BIM, point clouds and uncertainties

Meyer, Theresa; Brunn, Ansgar; Stilla, Uwe

doi:10.1016/j.autcon.2022.104442

Cited by 48 publications

(19 citation statements)

References 36 publications

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“…Kim [5] studied how to improve progress estimation considering recent trends in construction projects. Meyer [6] presented a methodology that not only accounted for the uncertainty present in indoor as-built point clouds (using the Dempster-Shafer evidence theory), but also considered the uncertainty of the indoor model that served as a reference. They used voxelization to label the state of each voxel and performed change detection by comparing the states in distinct measurement epochs.…”

Section: Applications 1progress Monitoringmentioning

confidence: 99%

“…Huang [27] proposed a methodology to calculate an effective scan range using mathematical reasoning, being able to reach a balance between scanning range and data size by estimating the appropriate angular resolution. Meyer [6] presented a methodology for change detection considering both the BIM uncertainty and the uncertainties of point clouds. The authors explicitly accounted for uncertainties using the Dempster-Shafer evidence theory.…”

Section: Dealing With Measurement Errorsmentioning

confidence: 99%

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Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

Abreu

Pinto

Matos

et al. 2023

IJGI

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Point cloud processing is an essential task in many applications in the AEC domain, such as automated progress assessment, quality control and 3D reconstruction. As much of the procedure used to process the point clouds is shared among these applications, we identify common processing steps and analyse relevant algorithms found in the literature published in the last 5 years. We start by describing current efforts on both progress and quality monitoring and their particular requirements. Then, in the context of those applications, we dive into the specific procedures related to processing point clouds acquired using laser scanners. An emphasis is given to the scan planning process, as it can greatly influence the data collection process and the quality of the data. The data collection phase is discussed, focusing on point cloud data acquired by laser scanning. Its operating mode is explained and the factors that influence its performance are detailed. Data preprocessing methodologies are presented, aiming to introduce techniques used in the literature to, among other aspects, increase the registration performance by identifying and removing redundant data. Geometry extraction techniques are described, concerning both interior and outdoor reconstruction, as well as currently used relationship representation structures. In the end, we identify certain gaps in the literature that may constitute interesting topics for future research. Based on this review, it is evident that a key limitation associated with both Scan-to-BIM and Scan-vs-BIM algorithms is handling missing data due to occlusion, which can be reduced by multi-platform sensor fusion and efficient scan planning. Another limitation is the lack of consideration for laser scanner performance characteristics when planning the scanning operation and the apparent disconnection between the planning and data collection stages. Furthermore, the lack of representative benchmark datasets is hindering proper comparison of Scan-to-BIM and Scan-vs-BIM techniques, as well as the integration of state-of-the-art deep-learning methods that can give a positive contribution in scene interpretation and modelling.

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Section: Applications 1progress Monitoringmentioning

confidence: 99%

Section: Dealing With Measurement Errorsmentioning

confidence: 99%

Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

Abreu

Pinto

Matos

et al. 2023

IJGI

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“…[26] According to the different carrying platforms, LiDAR can be further divided into airborne laser scanning (ALS), [27,28] mobile laser scanning (MLS), [29,30] and terrestrial laser scanning (TLS). [31] Point clouds for SAR are typically produced using single-channel radargrammetric SAR, [32] multi-channel interferometric SAR, [33] multichannel tomographic SAR, [34] or persistent scatterer interferometry (PSI) using spaceborne or aerial platforms. [35,36] These methods have been applied in the detection of urban building changes.…”

Section: Introductionmentioning

confidence: 99%

3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

Gao,

Guo,

Wang

et al. 2024

Advcd Theory and Sims

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Change detection methods for measuring deformation of the LNG storage tank's exterior wall cannot meet the requirements for extensive and comprehensive deformation analysis. The method proposed in this paper uses multi‐source spatial data to detect the deformation of the LNG storage tank's exterior wall. Exterior wall data collected simultaneously by terrestrial and airborne laser scanners, as well as close‐range and oblique photogrammetry, are pre‐processed separately and then registered to the global point clouds. The deformation state of exterior wall is represented by a thorough assessment approach that takes into account four change indices. The results suggest that the registration precision of global point clouds is 6 mm. The global ovality of the LNG storage tank is 0.053%, with a tilt change of 2.58° and a deviation of 27 mm in the direction of the azimuth of 321.98°, an average value of 0.07 mm for depression and protrusion deformation, and a surface flatness of 21.3 mm. In this case, the flatness is greater than the variation threshold of the standard specification, indicating that the tank may have structural uneven settlement or other deformation problems. The attraction of the proposed method resides in its capacity to efficiently and accurately obtain spatial positioning and color information of the LNG storage tank's exterior wall, thereby providing a robust foundation for precise judgments regarding the deformation state.

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“…Research by [24] establishes a fitting field for high-density point clouds to Gaussian random statistical point cloud models, employing fitting fields and Monte Carlo simulations (MCS) to estimate the probability distribution of size features. Meyer et al [25] consider the fusion of indoor scanning geometric shapes with BIM uncertainty and as-built point cloud uncertainty for precision assessment, achieving relatively reliable geometric evaluation and uncertainty interpretation.…”

Section: Introductionmentioning

confidence: 99%

LiDAR point cloud quality optimization method based on BIM and affine transformation

Liu,

Gao,

et al. 2023

Meas. Sci. Technol.

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Laser Detection and Ranging (LiDAR) systems possess the capability to generate high-resolution three-dimensional (3D) data of indoor environments. The inherent uncertainties pertaining to relative spatial positioning and the centimeter-level precision of LiDAR ranging, however, contribute to discernible constraints within contexts requiring elevated degrees of precision, particularly in the domain of high-precision sensing applications. In response to this concern, this paper introduces an approach designed to mitigate and appraise the uncertainty associated with plane positioning through the utilization of point cloud fitting methodologies, concurrently integrating principles of building information modeling (BIM) and anisotropic affine transformations (AAT). Primarily, the methodology involves the extraction of precise plane characteristics employing the tenets of robustly weighted total least squares theory within the context of point cloud fitting. Subsequently, the method synergistically incorporates geometric information emanating from the Building Information Model alongside the accurately determined plane positioning data derived from LiDAR point clouds via AAT. This integration markedly enhances the precision of the ranging system’s datasets. Ultimately, the assessment of ranging uncertainty is conducted by quantifying the deviations of individual points from the conforming plane and employing a probability approximative scheme grounded in higher-order moments. Experimental results demonstrate the method’s precision and efficacy, offering a solution to the challenge of achieving higher perception precision in LiDAR-based ranging systems.

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Change detection for indoor construction progress monitoring based on BIM, point clouds and uncertainties

Cited by 48 publications

References 36 publications

Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

LiDAR point cloud quality optimization method based on BIM and affine transformation

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