A novel robot hand-eye calibration method to enhance calibration accuracy based on the POE model

Abstract

Hand-eye calibration is crucial for vision-based robots using open-loop visual control. Based on the product of exponentials model, this study introduces a novel hand-eye calibration algorithm to improve calibration accuracy. As opposed to the traditional method of solving the AX = XB type in the hand-eye calibration using the marker, we transform the solution into an AX = B type without requiring accurate camera parameters. Firstly, the nominal marker pose in the robot base is described by the nominal kinematic parameters of the robot, camera, and end-effector-to-camera. The actual marker pose is obtained by physically contacting the marker with the probe. Subsequently, the error parameter of the hand-eye pose is defined as linear to the error between the nominal and actual marker poses and can be solved by an iterative least-squares algorithm. Finally, the calibrated hand-eye pose is acquired by multiplying its nominal pose and the exponential mapping of the error parameter. The simulations and real experiments illustrate the effectiveness and stability of the proposed algorithm. Noteworthy, the mean position accuracy of the vision-based robot is improved nearly 20 times after calibration. Furthermore, the comparison experiment demonstrates that the proposed algorithm is preferable for improving hand-eye calibration accuracy.