Robotic eye-in-hand calibration is the task of determining the rigid 6-DoF pose of the camera with respect to the robot end-effector frame. In this paper, we formulate this task as a non-linear optimization problem and introduce an active vision approach to strategically select the robot pose for maximizing calibration accuracy. Specifically, given an initial collection of measurement sets, our system first computes the calibration parameters and estimates the parameter uncertainties. We then predict the next robot pose from which to collect the next measurement that brings about the maximum information gain (uncertainty reduction) in the calibration parameters. We test our approach on a simulated dataset and validate the results on a real 6-axis robot manipulator. The results demonstrate that our approach can achieve accurate calibrations using many fewer viewpoints than other commonly used baseline calibration methods.

翻译：机器人眼在手标定是指确定相机相对于机器人末端执行器坐标系的六自由度刚体位姿的任务。本文将该任务建模为非线性优化问题，并提出一种主动视觉方法，用于策略性地选择机器人位姿以最大化标定精度。具体而言，给定初始测量集合，我们的系统首先计算标定参数并估计参数不确定性。随后，我们预测下一机器人位姿，以采集能带来标定参数最大信息增益（不确定性降低）的下一组测量数据。我们在仿真数据集上测试了该方法，并在真实六轴机器人操作臂上验证了结果。实验表明，与常用的基线标定方法相比，我们的方法能够使用更少的视角实现高精度标定。