Jin, G., Yu, X., Chen, Y., Li, J. (2024), A Practical Method For Hand-Eye Calibration Through Rotation Fusion, submitted to IEEE Transactions on Instrumentation and Measurement.
The hand-eye calibration problem is a fundamental problem in visual assisted robotics. Ensuring the calibration robustness is crucial as the calibration results are used in every task execution. Unlike traditional separate or simultaneous method, we propose a rotation fusion method to further improve the robustness. The proposed method is practical and includes four straightforward steps. First, the forward rotation estimate is obtained using the rotation of AX = XB. Second, the backward rotation estimate is achieved via the translation of AX = XB, which is not involved in existing methods. Third, the two rotation estimates are fused with identity covariance to cope with different noise conditions. Last, the translation estimate is obtained by a least-square minimization.
Figure: Signal flow diagrams of (a) the separate method, (b) the simultaneous method, and (c) our fusion method for hand-eye calibration.
The code runs on Matlab R2023a without any additional dependencies. The necessary auxiliary functions can be found in the "auxiliary" folder.
To run the fusion calibration, call
[R_out,t_out,rnti]=HECfuse(RAin,tAin,RBin,tBin)
where
RAin(3x3xn): rotation matrix of A,tAin(3xn): translation vector of A (unit: m),RBin(3x3xn): rotation matrix of B,tBin(3x3xn): translation vector of B (unit: m),R_out(3x3): rotation matrix of hand-eye pose,t_out(3x1): translation vector of hand-eye pose (unit: m),rnti(1×1)： runtime (unit: seconds).
The comparison methods include
HECrot: forward separate method,HECtran: backward separate method,HECsim: simultaneous method,HECTsai: Tsai's method,HECWu: Wu's method,HECSARA: Sarabandi's method.
Please refer to the submitted article or reference for details.
Demo main1, main2, and main3 correspond to the accuracy comparison of different types of methods, while Demo main4 is a comparison of computational efficiency. When the program ends, a visual result will be presented.
- Tsai R Y, Lenz R K. A new technique for fully autonomous and efficient 3 d robotics hand/eye calibration[J]. IEEE Transactions on robotics and automation, 1989, 5(3): 345-358.
- Wu J, Sun Y, Wang M, et al. Hand-eye calibration: 4-D procrustes analysis approach[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69(6): 2966-2981
- Sarabandi S, Porta J M, Thomas F. Hand-eye calibration made easy through a closed-form two-stage method[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 3679-3686.
Gumin Jin, Department of Automation, Shanghai Jiao Tong University, Shanghai, jingumin@sjtu.edu.cn