Accurate Virtual Trial Assembly Method of Prefabricated Steel Components Using Terrestrial Laser Scanning

Zhou, Yin; Han, Dongdong; Hu, Kaixin; Qin, Guocheng; Xiang, Zhongfu; Ying, Chunli; Zhao, Lidu; Hu, Xingyi

doi:10.1155/2021/9916859

Cited by 10 publications

(8 citation statements)

References 32 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the x-o-y plane of bridge TLS data should be an absolute horizontal plane. The dual-axis compensator of the Leica P50 scanner can achieve an accuracy of 1.5" [11]. We try to decorrelate the values based on the characteristics of the bridge point cloud in the future.…”

Section: The Results Of Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

et al. 2022

Self Cite

View full text Add to dashboard Cite

Point cloud registration (PCR) is a vital problem in remote sensing and computer vision, which has various important applications, such as 3D reconstruction, object recognition, and simultaneous localization and mapping (SLAM). Although scholars have investigated a variety of methods for PCR, the applications have been limited by low accuracy, high memory footprint, and slow speed, especially for dealing with a large number of point cloud data. To solve these problems, a novel local descriptor is proposed for efficient PCR. We formed a comprehensive description of local geometries with their statistical properties on a normal angle, dot product of query point normal and vector from the point to its neighborhood point, the distance between the query point and its neighborhood point, and curvature variation. Sub-features in descriptors were low-dimensional and computationally efficient. Moreover, we applied the optimized sample consensus (OSAC) algorithm to iteratively estimate the optimum transformation from point correspondences. OSAC is robust and practical for matching highly self-similar features. Experiments and comparisons with the commonly used descriptor were conducted on several synthetic datasets and our real scanned bridge data. The result of the simulation experiments showed that the rotation angle error was below 0.025° and the translation error was below 0.0035 m. The real dataset was terrestrial laser scanning (TLS) data of Sujiaba Bridge in Chongqing, China. The results showed the proposed descriptor successfully registered the practical TLS data with the smallest errors. The experiments demonstrate that the proposed method is fast with high alignment accuracy and achieves a better performance than previous commonly used methods.

show abstract

Section: The Results Of Experimentsmentioning

confidence: 99%

“…The iterative nearest point (ICP) algorithm is the most commonly applied PCR method because the iteration is simple, and the convergence speed is fast [11][12][13][14]. It iteratively calculates the optimum transformation until the function criterion converges to the specified value.…”

Section: Introductionmentioning

confidence: 99%

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Te outliers at the edges of the sector region interfere with the registration accuracy of the colored ICP. Tis is because the principle of the method is to fnd the nearest point pair in two point clouds based on the intensity parameter [56]. Te extraction accuracy of the algorithm remains below 1.3 mm for four resolutions, with slight fuctuations in accuracy.…”

Section: Algorithm Evaluationmentioning

confidence: 98%

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Liang,

Zhang,

Zhang

et al. 2024

Structural Control and Health Monitoring

View full text Add to dashboard Cite

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking.

show abstract

“…Laefer and Hong [23] proposed a kernel density estimation method to reconstruct the point cloud of standard steel beams in a BIMcompatible format. Zhou et al [24] developed a parameter extraction method based on grid points to extract quickly and efectively bolt hole features. As for obtaining a 3D sweep path of point clouds, the line shape of the curve component with respect to a straight line is difcult to extract.…”

Section: Geometric Information Extractionmentioning

confidence: 99%

Semi-automated Generation of Geometric Digital Twin for Bridge Based on Terrestrial Laser Scanning Data

Hu¹,

Han

Qin

et al. 2023

Advances in Civil Engineering

Self Cite

View full text Add to dashboard Cite

Digital twins (DTs) have a great potential for bridge operation and maintenance. Geometric digital twins (gDTs) are the key component of DTs. At present, a growing number of researchers are using high-precision 3D laser point clouds to generate gDTs. However, for large bridges, such as arch, cable-stayed, and suspension bridges, comprehensive point-cloud collection stations are difficult to set up due to their large span, narrow site, and limited field of vision. Consequently, the complete point clouds of these bridges cannot be easily obtained. Thus, knowing how to process absence point clouds and generate gDTs is an urgent problem. This study proposes a semiautomatic method of extracting geometric information of a bridge’s components in the absence of point clouds. First, an algorithm based on the combination of the iterative polynomial fitting curve and sliding window is developed to extract the arch ring accurately. Second, an improved random sample consensus (RANSAC) algorithm based on distribution density is adopted to extract the cross sections of the arch bridge components, except the arch ring. For cross sections that lack point clouds, a translation strategy is used to supplement the unknown line segment. Finally, for the T-beam, a model alignment method is proposed to best match the characteristic intersections extracted by the improved RANSAC algorithm and the points corresponding to the design model. The quality of the generated models is gauged using a point cloud deviation chromatogram. In addition, the stressed component piers are compared with its design parameters to verify the accuracy of the proposed method. Results show that our method can efficiently and accurately extract geometric information and generate gDT for the bridge.

show abstract

Accurate Virtual Trial Assembly Method of Prefabricated Steel Components Using Terrestrial Laser Scanning

Cited by 10 publications

References 32 publications

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Semi-automated Generation of Geometric Digital Twin for Bridge Based on Terrestrial Laser Scanning Data

Contact Info

Product

Resources

About