基于误差分布估计的机器人手眼标定方法研究

精确的机器人手眼标定对于机器人的视觉环境感知具有重要的意义。现有算法通常采用最小二乘估计或全局非线性优化求解方法对机器人手眼系统的变换参数进行估计。当系统存在测量粗差时直接采用最小二乘估计会导致标定结果精度的下降；基于全局非线性优化策略的标定算法则由于数据粗差的影响，求解过程易过早收敛也会造成标定精度低。为了解决误差粗差敏感的问题，提出了一种基于误差分布估计的加权最小二乘鲁棒估计方法，以提高机器人手眼标定的精度。首先，通过最小二乘估计计算手眼变换矩阵；之后计算每对坐标对应的误差值；根据误差值的分布概率初始化对应坐标数据的权值；最后采用加权的最小二乘估计重新计算机器人手眼标定矩阵。最后引入迭代估计策略进一步提高手眼标定的精度。设计的机器人手眼标定实验及结果证明，所提算法能够在数据粗差影响下保持较高的标定精度，更适用于机器人的手眼标定问题。

Error Distribution Estimation Based Robot Hand-eye Calibration

Accurate hand-eye calibration is a very significant task in robotics. Many algorithms employ Least Square Estimation (LSE) and global nonlinear optimization to deal with this problem. LSE can lead to low calibration precision when gross errors occur. On the other hand, global nonlinear optimization strategy is easy to converge because of the influence of gross error. Different from the existing approaches, an error distribution estimation based weighted LSE is proposed for the robot hand-eye calibration task. Firstly, transformation matrix is computed by traditional LSE. Error distribution is estimated and the data is weighted according to the density estimation. Then the fine result can be conducted based on the weighted data. Lastly, iteration scheme is proposed to further improve the calibration accuracy. To evaluate the proposed method, an experiment was designed and the test result demonstrates the robustness of the proposed approach.