Cylinder-based simultaneous registration and model fitting of laser-scanned point clouds for accurate as-built modeling of piping system

Moritani, Ryota; Kanai, Satoshi; Date, Hiroaki; Watanabe, Masahiro; Nakano, Takahiro; Yamauchi, Yuta

doi:10.1080/16864360.2018.1441239

Cited by 17 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The registration process, however, only uses planar surfaces. Point to plane registration is used particularly since it is known to be accurate [40], and reliable closed-form linear solution to the point to plane registration problem already exists [41] -unlike say cylinder-based registration, which is non-linear at best [42].…”

Section: A New Generic Process For Point Cloud Vs Modelmentioning

confidence: 99%

Field Information Modeling (FIM)™: Best Practices using Point Clouds

Maalek¹

2021

Preprint

View full text Add to dashboard Cite

This study presents established methods, along with new algorithmic developments, to automate the point cloud processing within the Field Information Modeling (FIM)™ framework. More specifically, given an n-D designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of the point clouds to their corresponding elements within the designed model is considered. The methods addressed two classes of field conditions, namely, (i) negligible construction errors; and (ii) existence of construction errors. The emphasis was given to describing and defining the assumptions in each method and shed light on some of their potentials and limitations in practical settings. Considering the shortcomings of current point cloud processing frameworks, three new and generic algorithms were developed to help solve the point cloud to model assignment in field conditions with both negligible, and existence (or speculation) of construction errors. The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results.

show abstract

Section: A New Generic Process For Point Cloud Vs Modelmentioning

confidence: 99%

Field Information Modeling (FIM)™: Best Practices using Point Clouds

Maalek¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The alignment was performed using the RANSAC approach and a hash table. In our previous paper [21], we used a one-dimensional hash table, where the distance between two cylinders was adopted as a hash key to avoid exhaustive searching for all cylinder pair combinations and to determine the corresponding cylinders in an efficient manner. However, in plants, many pipes are installed at equal distances from each other; therefore, the probability of key collision is increased, and the efficiency of the search for the corresponding cylinders remains low.…”

Section: Efficient Coarse Scan Registration Based On Three-dimensional Hash Tablementioning

confidence: 99%

“…The first drawback concerns the excessively large point clouds measured on a large-scale piping system, which make coarse registration inefficient. The proposed coarse registration in [21] is needed to find the initial scanner positions and the initial corresponding cylinders between several scans, but the processing time increased rapidly as the number of scanners and extracted cylinders increased, e.g., 2.4 h with only two scans and a few hundred cylinders. In case of more practical periodic renovation works, the processing time to build three-dimensional as-built model should finish at least overnight.…”

Section: Introductionmentioning

confidence: 99%

A Compact Face-Based Topological Data Structure for Triangle Mesh Representation

Zhang¹,

Luo²,

Jia³

2018

CAD&A

View full text Add to dashboard Cite

In this work, we improved the efficiency and accuracy of our proposed cylinder-based registration and model fitting method of point clouds by terrestrial laser scanners for the as-built modeling of piping systems. Our algorithm simultaneously calculated the scanner parameters and cylinder parameters to avoid the propagation of registration errors and modeling errors. Coarse registration is performed by finding the alignment of the cylinder axes based on the random sample consensus approach and using a hash table. The efficiency of the coarse registration is improved by introducing a three-dimensional hash table. The fine registration and modeling is performed by minimizing the fitting errors of the cylinders as a nonlinear function of the positional and geometric parameters of scanners and cylinders. An iteratively reweighted least squares method is applied to the fine registration and modeling, leading to improved robustness. Moreover, for the modeling of pipes that are slightly bent due to gravity, incident angle filtering of scanned points and cylinder subdivision of the pipes to be modeled are introduced. The efficiency and robustness of the improved algorithm were compared with the previous approach using both artificial and real point clouds. The effectiveness of incident angle filtering and cylinder subdivision was confirmed. The proposed algorithm achieved the level of cylindrical modeling precision required for the renovation work of piping systems.

show abstract

“…The registration process, however, only uses planar surfaces. Point-to-plane registration is used particularly since it is known to be accurate [50], and a reliable closed-form linear solution to the point-to-plane registration problem already exists [51], unlike, say, cylinder-based registration, which is non-linear at best [52]. Step 1 of Algorithm 1 involves an iterative process to improve the registration in the absence of a reliable initial target-based registration (similar to the ICP of [20]).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Field Information Modeling (FIM)™: Best Practices Using Point Clouds

Maalek

2021

Remote Sensing

View full text Add to dashboard Cite

This study presented established methods, along with new algorithmic developments, to automate point cloud processing in support of the Field Information Modeling (FIM)™ framework. More specifically, given a multi-dimensional (n-D) designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of point clouds to their corresponding model elements was considered. The methods addressed two classes of field conditions, namely (i) negligible construction errors and (ii) the existence of construction errors. Emphasis was given to defining the assumptions, potentials, and limitations of each method in practical settings. Considering the shortcomings of current frameworks, three generic algorithms were designed to address the point-cloud-to-model assignment. The algorithms include new developments for (i) point cloud vs. model comparison (negligible construction errors), (ii) robust point neighborhood definition, and (iii) Monte-Carlo-based point-cloud-to-model surface hypothesis testing (existence of construction errors). The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results. For the overall problem of point-cloud-to-model assignment, the proposed point cloud vs. model and point-cloud-to-model hypothesis testing methods achieved F-measures of 99.3% and 98.4%, respectively, on real-world datasets.

show abstract

Cylinder-based simultaneous registration and model fitting of laser-scanned point clouds for accurate as-built modeling of piping system

Cited by 17 publications

References 18 publications

Field Information Modeling (FIM)™: Best Practices using Point Clouds

Field Information Modeling (FIM)™: Best Practices using Point Clouds

A Compact Face-Based Topological Data Structure for Triangle Mesh Representation

Field Information Modeling (FIM)™: Best Practices Using Point Clouds

Contact Info

Product

Resources

About