A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration

Hongchen Chen; Xie Zhang; Shaoyi Du; Zongze Wu; Nanning Zheng

doi:10.1109/JAS.2019.1911579

Volume 6 Issue 4

Jul. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(4): 981-991

Hongchen Chen, Xie Zhang, Shaoyi Du, Zongze Wu and Nanning Zheng, "A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 981-991, July 2019. doi: 10.1109/JAS.2019.1911579

Citation:

Hongchen Chen, Xie Zhang, Shaoyi Du, Zongze Wu and Nanning Zheng, "A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 981-991, July 2019. doi: 10.1109/JAS.2019.1911579

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A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration

doi: 10.1109/JAS.2019.1911579

Funds: This work was supported in part by the National Natural Science Foundation of China (61627811, 61573274, 61673126, U1701261)

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Author Bio:
Hongchen Chen received the bachelor degree in electronic information engineering from Xidian University, China in 2016. He is currently a Ph.D. candidate at the School of Electronic and Information, South China University of Technology. His research interests include image registration and radio frequency integrated circuit

Xie Zhang is a Senior Experimentalist of South China University of Technology. She received the Ph.D. degree in signal and information processing from South China University of Technology. Her research interests include signal processing, image processing, and computer systems

Shaoyi Du received the B.S. degrees both in computational mathematics and in computer science, M.S. degree in applied mathematics and Ph.D. degree in pattern recognition and intelligence system from Xi’an Jiaotong University, China in 2002, 2005 and 2009, respectively. He worked as a postdoctoral fellow in Xi’an Jiaotong University from 2009 to 2011 and visited University of North Carolina at Chapel Hill from 2013 to 2014. He is currently an Associate Professor of the Institute of Artificial Intelligence and Robotics in Xi’an Jiaotong University. His research interests include image registration, mobile robot, and object detection and recognition

Zongze Wu received the B.S. degree in materials processing engineering, M.S. and Ph.D. degrees both in control science and engineering, from Xi’an Jiaotong University, China in 1999, 2003 and 2005, respectively. He is a Professor of the College of Automation in Guangdong University of Technology. His research interests include pattern recognition, image/video coding, and internet of things

Nanning Zheng (SM’93–F’05) graduated from the Department of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China, in 1975, and received the M.S. degree in information and control engineering from Xi’an Jiaotong University in 1981 and the Ph.D. degree in electrical engineering from Keio University, Yokohama, Japan, in 1985. He jointed Xi’an Jiaotong University in 1975, and he is currently a Professor and the Director of the Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University. Since August 2003, he has been the President of Xi’an Jiaotong University. His research interests include computer vision, pattern recognition, machine vision and image processing, neural networks, and hardware implementation of intelligent systems. Dr. Zheng became a member of the Chinese Academy of Engineering in 1999 and has been the chief scientist and the director of the Information Technology Committee of the China National High Technology Research and Development Program since 2001. He was General Chair of the International Symposium on Information Theory and Its Applications and General Co-Chair of the International Symposium on Nonlinear Theory and Its Applications, both in 2002. He is a member of the Board of Governors of the IEEE ITS Society and the Chinese Representative on the Governing Board of the International Association for Pattern Recognition. He also serves as an executive deputy editor of the Chinese Science Bulletin
Corresponding author: X. Zhang is with the School of Electric Power, South China University of Technology, Guangzhou 510640, China (e-mail: zhangxie@scut.edu.cn)
Received Date: 2018-10-16
Revised Date: 2018-12-24
Accepted Date: 2019-02-03

Available Online: 2019-05-27

Abstract

Abstract

The iterative closest point (ICP) algorithm has the advantages of high accuracy and fast speed for point set registration, but it performs poorly when the point set has a large number of noisy outliers. To solve this problem, we propose a new affine registration algorithm based on correntropy which works well in the affine registration of point sets with outliers. Firstly, we substitute the traditional measure of least squares with a maximum correntropy criterion to build a new registration model, which can avoid the influence of outliers. To maximize the objective function, we then propose a robust affine ICP algorithm. At each iteration of this new algorithm, we set up the index mapping of two point sets according to the known transformation, and then compute the closed-form solution of the new transformation according to the known index mapping. Similar to the traditional ICP algorithm, our algorithm converges to a local maximum monotonously for any given initial value. Finally, the robustness and high efficiency of affine ICP algorithm based on correntropy are demonstrated by 2D and 3D point set registration experiments.
- Affine iterative closest point (ICP) algorithm,
- correntropy-based,
- robust point set registration

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References

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Correntropy is introduced to our algorithm to suppress the outliers and noises.
The one-to-one index mapping is employed for fast speed.
The closed-form solution of our algorithm is presented to reduce the run-time.
Our algorithm is independent of feature extraction and can be used with other methods.

A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration

doi: 10.1109/JAS.2019.1911579

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