High-precision point cloud registration system of multi-view industrial self-similar workpiece based on super-point space guidance

Xingguo Wang (), Xiaoyu Chen (), Zhuang Zhao (), Yi Zhang (), Dongliang Zheng () and Jing Han ()
Additional contact information
Xingguo Wang: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology
Xiaoyu Chen: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology
Zhuang Zhao: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology
Yi Zhang: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology
Dongliang Zheng: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology
Jing Han: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 18, 1765-1779

Abstract: Abstract The demand for 3D information in intelligent manufacturing makes complete point cloud of large workpiece increasingly important in the industrial field. However, due to the limited measurement range, the existing 3D reconstruction methods are diffcult to measure the large workpiece. The self-similar structure of workpiece also results in the low performance of existing 3D registration methods. To address the above problems, the point cloud registration system based on super-point space guidance is proposed by combining fringe projection profilometry (FPP) and point cloud registration technology to register multi-view point clouds of large workpiece. Specifically, to reduce the impact of self-similar structure on registration, we utilize spatial compatibility to partition the point clouds into local super-point space pairs, based on which to guide the multi-scale feature extraction network (MFENet) to mine effective super-point features, then the super-point features with high confidence is selected to estimate the optimal pose matrix. Experimental results show our registration error measured by standard ball is 0.024 mm, and the point cloud of large workpiece we measured reach the accuracy level of laser tracker. In addition, the registration recall of our system at higher accuracy thresholds is 95%, which demonstrates the high reliability of the method for accuracy-critical applications.

Keywords: Structure light; Point cloud registration; Large and self-similar workpiece; Super point; Feature extraction (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02136-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02136-x

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02136-x

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().