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An effective point cloud registration method for three-dimensional reconstruction of pressure piping

Published online by Cambridge University Press:  16 May 2024

Yulong Zhang Open the ORCID record for Yulong Zhang [Opens in a new window] ,
Enguang Guan ,
Baoyu Wang Open the ORCID record for Baoyu Wang [Opens in a new window]  and
Yanzheng Zhao
Yulong Zhang*
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Enguang Guan
Affiliation:
College of Logistics Engineering, Shanghai Maritime University, Shanghai, China
Baoyu Wang
Affiliation:
College of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, China
Yanzheng Zhao
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
*
Corresponding author: Yulong Zhang; Email: jerry_zhang@sjtu.edu.cn
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Abstract

At present, industrial scenes with sparse features and weak textures are widely encountered, and the three-dimensional reconstruction of such scenes is a recognized problem. Pressure pipelines have a wide range of applications in fields such as petroleum engineering, chemical engineering, and hydropower station engineering. However, there is no mature solution for the three-dimensional reconstruction of pressure pipes. The main reason is that the typical scenes in which pressure pipes are found also have relatively few features and textures. Traditional three-dimensional reconstruction algorithms based on feature extraction are largely ineffective for such scenes that are lacking in features. In view of the above problems, this paper proposes an improved interframe registration algorithm based on point cloud fitting with cylinder axis vector constraints. By incorporating geometric feature parameters of a cylindrical pressure pipeline, specifically the axis vector of the cylinder, to constrain the traditional iterative closest point algorithm, the accuracy of point cloud registration can be improved in scenarios lacking features and textures, and some environmental uncertainties can be overcome. Finally, using actual laser point cloud data collected from pressure pipelines, the proposed fitting-based point cloud registration algorithm with cylinder axis vector constraints is tested. The experimental results show that under the same conditions, compared with other open-source point cloud registration algorithms, the proposed method can achieve higher registration accuracy. Moreover, integrating this algorithm into an open-source three-dimensional reconstruction algorithm framework can lead to better reconstruction results.

Keywords

sparse featuresthree-dimensional reconstructionpoint cloud registrationcylindrical fittingpressure pipelines
Type
Research Article
Information
Robotica , First View , pp. 1 - 18
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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