基于无极卡尔曼滤波算法的雅可比矩阵估计

在基于图像的机器人视觉伺服中，采用在线估计图像雅可比的方法，不需事先知道系统的精确模型，可以避免复杂的系统标定过程。为了有效改善图像雅可比矩阵的在线估计精度,进而提高机器人的跟踪精度,针对机器人跟踪运动目标的应用背景,提出了利用无极卡尔曼滤波算法在线估计总雅可比矩阵。在二自由度的机器人视觉伺服仿真平台上，分别用卡尔曼滤波器（KF）、粒子滤波器（PF）和无极卡尔曼滤波器（UKF）三种算法进行总雅可比矩阵的在线估计。实验结果证明，使用UKF算法的跟踪精度优于其他两种算法,时间耗费仅次于KF算法。

Unscented Kalman filter for on-line estimation of Jacobian matrix

In image based robot visual servo system， image Jacobian matrix is commonly used for calibration. Using on-line image Jacobian matrix estimation method， the complex system calibration can be avoided without knowing the accurate system models. In this paper， the author proposed to use the Unscented Kalman Filter (UKF) for on-line estimation of total Jacobian matrix for the sake of improving the tracking accuracy of the robots which is tracking a moving object. In order to evaluate the performance， three algorithms using Kalman Filter (KF), Particle Filter (PF), and UKF were used for total Jocobian matrix estimation in a 2-Degree Of Freedom (DOF) robot visual servo platform. The experimental results show that the UKF algorithm outperforms the other two in accuracy while its time cost is very much close to the KF algorithm.