Browse > Article
http://dx.doi.org/10.6109/jkiice.2021.25.6.799

Parameter analysis in Fast Global Registration to improve accuracy and speed  

Lim, Sukhyun (Innovation Center, 3D Systems Korea)
Abstract
The transforming process of point clouds with its local coordinates into a global coordinate is called registration. In contrast to the local registration which takes a long time to calculate and performs precision registration after initial rough positioning, the global registration calculates the corresponding points for registration and performs at once, so it is generally faster than the local registration, and can perform it regardless of the initial position. Among the global methods, the Fast Global Registration is one of the widely used methods due to its fast performance. However, lots of parameters should be set to increase the registration accuracy and speed. In this paper, after analyzing and experimenting the parameters and propose parameters that work effectively in actual registration. The proposed result will be helpful in setting the direction when it is necessary to use the Fast Global Registration method.
Keywords
Registration; ICP; RANSAC; Fast global registration;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. He, L. Huang, B. Zhao, B. Chen, and B. Hu, "Advanced functional materials in solid phase extraction for ICP-MS determination of trace elements and their species - A review," Analytica Chimica Acta, vol. 973, no. 22 pp. 1-24, 2017.   DOI
2 Z. Wu, H. Chen, S. Du, M. Fu, N. Zhou, and N. Zheng, "Correntropy based scale ICP algorithm for robust point set registration," Pattern Recognition, vol. 93, pp. 14-24, 2019.   DOI
3 J. Yang, H. Li, D. Campbell, and Y. Jia, "Go-ICP: A globally optimal solution to 3D ICP pointset registration," in Proceedings IEEE/CVF International Conference on Computer Vision, 2016.
4 Q. Y. Zhou, P. Jaesik, and K. Vladlen, "Fast global registration," in Proceedings European Conference on Computer Vision, Netherlands, 2016.
5 Open3D project [Internet]. Available: http://www.open3d.org/.
6 R. B. Rusu, N. Blodow, and M. Beetz, "Fast point feature histograms (FPFH) for 3D registration," in Proceedings IEEE International Conference on Robotics and Automation, 2009.
7 J. T. Barron, "A general and adaptive robust loss function," in Proceeding of Computer Vision and Pattern Recognition, USA, pp. 4331-4339, 2019.
8 Stanford University 3D Scan Repository [Internet]. Available: http://graphics.stanford.edu/data/.
9 S. Lim, "Effective criterion for evaluating registration accuracy," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no. 5, pp. 652-658, 2021.   DOI
10 K. Kwon, "A weighted points registration method to analyze dimensional errors occurring during shipbuilding process," Transactions of the Society of CAD/CAM Engineers, vol. 21, no. 2, pp. 151-158, 2016.   DOI
11 Z. H. Nejad and M. Nasri, "An adaptive image registration method based on SIFT features and RANSAC transform," Computers & Electrical Engineering, vol. 62, pp. 524-537, 2017.   DOI