Hand–eye calibration problem
In robotics and mathematics, the hand eye calibration problem, or robot-sensor calibration problem, is the problem of determining the transformation between a robot end-effector and a camera or between a robot base and the world coordinate system.[1] It takes the form of AX=ZB, where A and B are two systems, usually a robot base and a camera, and X and Z are unknown transformation matrices. A highly studied special case of the problem occurs where X=Z, taking the form of the problem AX=XB. Solutions to the problem take the forms of several types of methods, including "separable closed-form solutions, simultaneous closed-form solutions, and iterative solutions".[2] The covariance of X in the equation can be calculated for any randomly perturbed matrices A and B.[3]
Methods
Many different methods and solutions developed to solve the problem, broadly defined as either Separable, simultaneous solutions. Each type of solution has specific advantages and disadvantages as well as formulations and applications to the problem. A common theme throughout all of the methods is the common use of quaternions to represent rotation matrices.
Separable solutions
Given the equation AX=ZB, it is possible to decompose the equation into a purely rotational and translational part; methods utilizing this are referred to as separable methods. Where RA represents a 3×3 rotation matrix and tA a 3×1 translation vector, the equation can be broken into two parts:[4]
- RARX=RZRB
- RAtX+tA=RZtB+tZ
Equation 2 becomes linear if RZ is known. As such, the most frequent approach is to Rx and Rz using the first equation then using it to solve for the second two variables in the second equation. Rotation is represented using quaternions, allowing for a linear solution to be found. While separable methods are useful, any error in the estimation for the rotation matrices is compounded when being applied to the translation vector.[5] Other solutions avoid this problem.
Simultaneous solutions
Simultaneous solutions are based on solving for both X and Z at the same time (rather than basing the solution of one part off of the other as in separable solutions), propagation of error is significantly reduced.[6] By formulating the matrices as dual quaternions, it is possible to get a linear equation by which X is solvable in a linear format.[5] An alternative way applies the least-squares method to the Kronecker product of the matrices A⊗B. As confirmed by experimental results, simultaneous solutions have less error than separable quaternion solutions.[6]
Iterative solutions
Iterative solutions are another method used to solve the problem of error propagation. One example of an iterative solution is a program based on minimizing ||AX−XB||. As the program iterates, it will converge on a solution to X independent to the initial robot orientation of RB. Solutions can also be two-step iterative processes, and like simultaneous solutions can also decompose the equations into dual quaternions.[7] However, while iterative solutions to the problem are generally simultaneous and accurate, they can be computationally taxing to carry out and may not always converge on the optimal solution.[5]
References
- ^ Amy Tabb, Khalil M. Ahmad Yousef. "Solving the Robot-World Hand-Eye(s) Calibration Problem with Iterative Methods." 29 Jul 2019.
- ^ Mili I. Shah, Roger D. Eastman, Tsai Hong Hong. "An Overview of Robot-Sensor Calibration Methods for Evaluation of Perception Systems." 22 March, 2012
- ^ Huy Nguyen, Quang-Cuong Pham. "On the covariance of X in AX = XB." 12 June, 2017.
- ^ [1]
- ^ a b c Mili Shah, et al. "An Overview of Robot-Sensor Calibration Methods for Evaluation of Perception Systems."
- ^ a b Algo Li, et al. "Simultaneous robot-world and hand-eye calibration using dual-quaternions and Kronecker product." International Journal of the Physical Sciences Vol. 5(10), pp. 1530-1536, 4 September, 2010.
- ^ Zhiqiang Zhang, et al. "A computationally efficient method for hand–eye calibration." 19 July, 2017.