Evidence map›Paper›PMID 32824328›Full record

ArticleSensors (Basel, Switzerland)2020

Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems.

Ting On Chan, Linyuan Xia, Derek D Lichti, Yeran Sun, Jun Wang, Tao Jiang, Qianxia Li

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 3 countries.

Ting On ChanGuangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510000, China.ORCID 0000-0002-3318-7540
Linyuan XiaGuangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510000, China.
Derek D LichtiDepartment of Geomatics Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada.ORCID 0000-0001-7038-073X
Yeran SunDepartment of Geography, College of Science, Swansea University, Swansea SA28PP, UK.
Jun WangSchool of Electrical and Computer Engineering, Nanfang College of Sun Yat-sen University, Guangzhou 510000, China.
Tao JiangGuangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510000, China.
Qianxia LiGuangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510000, China.
Sun Yat-sen University · CNSwansea University · GBUniversity of Calgary · CA

Funding

Fundamental Research Funds for the Central Universities 37000-31610444
6 · The paper itself

Abstract

Pipe elbow joints exist in almost every piping system supporting many important applications such as clean water supply. However, spatial information of the elbow joints is rarely extracted and analyzed from observations such as point cloud data obtained from laser scanning due to lack of a complete geometric model that can be applied to different types of joints. In this paper, we proposed a novel geometric model and several model adaptions for typical elbow joints including the 90° and 45° types, which facilitates the use of 3D point clouds of the elbow joints collected from laser scanning. The model comprises translational, rotational, and dimensional parameters, which can be used not only for monitoring the joints' geometry but also other applications such as point cloud registrations. Both simulated and real datasets were used to verify the model, and two applications derived from the proposed model (point cloud registration and mounting bracket detection) were shown. The results of the geometric fitting of the simulated datasets suggest that the model can accurately recover the geometry of the joint with very low translational (0.3 mm) and rotational (0.064°) errors when ±0.02 m random errors were introduced to coordinates of a simulated 90° joint (with diameter equal to 0.2 m). The fitting of the real datasets suggests that the accuracy of the diameter estimate reaches 97.2%. The joint-based registration accuracy reaches sub-decimeter and sub-degree levels for the translational and rotational parameters, respectively.

Indexed as

elbow jointsgeometric modellaser scanningpoint cloud registration

Identifiers

PMID32824328
PMCPMC7471979
OpenAlexW3048986172

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.