Research on Hand–Eye Calibration Accuracy Improvement Method Based on Iterative Closest Point Algorithm

Tingwu Yan, Peijuan Li (), Yiting Liu, Tong Jia, Hanqi Yu and Guangming Chen
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Tingwu Yan: College of Mechanical Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Peijuan Li: Industrial Center, College of Innovation and Entrepreneurship, Nanjing Institute of Technology, Nanjing 211167, China
Yiting Liu: College of Automation, Nanjing Institute of Technology, Nanjing 211167, China
Tong Jia: College of Automation, Nanjing Institute of Technology, Nanjing 211167, China
Hanqi Yu: Industrial Center, College of Innovation and Entrepreneurship, Nanjing Institute of Technology, Nanjing 211167, China
Guangming Chen: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2023, vol. 13, issue 10, 1-14

Abstract: In the functioning of the hand–eye collaboration of an apple picking robot, the accuracy of the hand–eye relationship is a key factor affecting the efficiency and accuracy of the robot’s operation. In order to enhance the low accuracy of traditional hand–eye calibration methods, linear and nonlinear solving methods based on mathematical tools such as quaternions are commonly adopted. To solve the loss of accuracy in decoupling during the linearization solution and to reduce the cumulative error that occurs during nonlinear solutions, a hand–eye calibration method, based on the ICP algorithm, is proposed in this paper. The method initializes the ICP matching algorithm with a solution derived from Tsai–Lenz, and substitutes it for iterative computation, thereby ascertaining a precise hand–eye conversion relationship by optimizing the error threshold and iteration count in the ICP matching process. Experimental results demonstrate that the ICP-based hand–eye calibration optimization algorithm not only circumvents the issues pertaining to accuracy loss and significant errors during solving, but also enhances the rotation accuracy by 13.6% and the translation accuracy by 2.47% compared with the work presented by Tsai–Lenz.

Keywords: picking robots; hand–eye calibration; ICP matching algorithm; Tsai–Lenz (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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