Hand-eye calibration is a fundamental step for a robot equipped with vision systems. However, this problem usually interacts with robot calibration because robot geometric parameters are not very precise. In this article, a new calibration method considering the rotation parameters of the robot pose is proposed. First, a constraint least square model is established assuming that each spherical center measurement of standard ball is equal in the robot base frame, which provides an initial solution. To further improve the solution accuracy, a nonlinear calibration model in the sensor frame is established. Since it can reduce one error accumulation process, a more accurate reference point can be used for optimization. Then, the rotation parameters of the robot pose whose slight errors cause large disturbance to the solution are selected by analyzing the coefficient matrices of the error items. Finally, the hand-eye transformation parameters are refined together with the rotation parameters in the nonlinear optimization solution. Some comparative simulations are performed between the modified least square method, constrained least square method, and the proposed method. The experiments are conducted on a 5-axis hybrid robot named TriMule to demonstrate the superior accuracy of the proposed method.

中文翻译：

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考虑机器人位姿旋转参数的三维视觉传感器手眼标定方法

手眼校准是配备视觉系统的机器人的基本步骤。然而，这个问题通常与机器人校准相互作用，因为机器人几何参数不是很精确。在本文中，提出了一种考虑机器人位姿旋转参数的新标定方法。首先建立约束最小二乘模型，假设标准球的每个球心测量在机器人基础框架中都相等，从而提供了初始解。为了进一步提高求解精度，建立了传感器坐标系中的非线性标定模型。由于它可以减少一个错误累积过程，因此可以使用更准确的参考点进行优化。然后，通过分析误差项的系数矩阵，选取微小误差对解扰动较大的机器人位姿旋转参数。最后，手眼变换参数与非线性优化解中的旋转参数一起被细化。在改进的最小二乘法、约束最小二乘法和所提出的方法之间进行了一些比较模拟。实验在名为 TriMule 的 5 轴混合机器人上进行，以证明所提出方法的优越精度。约束最小二乘法和所提出的方法。实验在名为 TriMule 的 5 轴混合机器人上进行，以证明所提出方法的优越精度。约束最小二乘法和所提出的方法。实验是在名为 TriMule 的 5 轴混合机器人上进行的，以证明所提出方法的优越精度。