Solving the Robot-World Hand-Eye(s) Calibration Problem with Iterative Methods
This work addresses a critical calibration problem for robot manipulation tasks, particularly in vision applications, but it is incremental as it builds upon existing formulations with new parameterizations and cost functions.
The paper tackled the robot-world hand-eye calibration problem by developing a collection of iterative methods, which achieved greater accuracy on many metrics compared to seven state-of-the-art methods in real and simulated datasets.
Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end-effector and a camera, as well as the transformation between the robot base and the world coordinate system. This relationship has been modeled as $\mathbf{AX}=\mathbf{ZB}$, where $\mathbf{X}$ and $\mathbf{Z}$ are unknown homogeneous transformation matrices. The successful execution of many robot manipulation tasks depends on determining these matrices accurately, and we are particularly interested in the use of calibration for use in vision tasks. In this work, we describe a collection of methods consisting of two cost function classes, three different parameterizations of rotation components, and separable versus simultaneous formulations. We explore the behavior of this collection of methods on real datasets and simulated datasets, and compare to seven other state-of-the-art methods. Our collection of methods return greater accuracy on many metrics as compared to the state-of-the-art. The collection of methods is extended to the problem of robot-world hand-multiple-eye calibration, and results are shown with two and three cameras mounted on the same robot.