Abstract
In this paper, we propose two novel methods for robot-world-hand–eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called ‘hand–eye’ and ‘robot-world-hand–eye’, respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.
Highlights
Hand–eye calibration is an essential component of vision-based robot control known as visual servoing
We present a collection of iterative methods for the hand–eye calibration problem under both AX = XB and AX = ZB formulations
In order to assess the performance of the robot-world-hand–eye calibration methods, we present multiple datasets to test the methods in laboratory and near field settings
Summary
Hand–eye calibration is an essential component of vision-based robot control known as visual servoing. Shiu and Ahmed [4] presented a closed-form approach to finding the solution for the problem formulation AX = XB by separately estimating the rotation and translation from robot wrist to the camera in that order. X(b T ) and Z(t T ) are the homogenous transformations from robot base to the world frame and the optimize the camera’s intrinsic parameters using the nonlinear solver to yield better results. The common approach is to use a calibration pattern for simultaneously calculating overall accuracy of the system To compensate for these errors, the process must include additional the calibration parameters of the camera and the pose of the camera against the pattern or in this case steps to mitigate the effects. The camera calibration approach used in this study is based on the widely adopted method by Zhang [25]
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