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.

No abstract

This paper proposes a novel approach aimed at estimating the pose of a camera, affixed to a robotic manipulator, against a target object. Our approach provides a way to exploit the redundancy of the robotic arm kinematics by directly considering manipulator poses in the model formulation for camera pose estimation. We adopt a single camera multi-shot technique that minimizes the reprojection error over all the rigid poses. The results of the proposed method are compared to four other studies employing either monocular or stereo setup. The experimental results on synthetic and real data show that the proposed monocular approach achieves better and in some cases comparable results to the stereo approach. Moreover, the proposed approach is significantly more robust and precise compared to other methods.

No 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.