Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter
Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter
复制标题
使用自适应衰落卡尔曼滤波器的机器人机械手动态视觉跟踪
DOI：
10.1109/access.2020.2973299
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Yili Fu
中科院分区：
文献类型：
--
作者：
Jiadi Qu;Fuhai Zhang;Yunxi Tang;Yili Fu
针对图像特征暂时遮挡情况下运动目标的视觉跟踪问题，采用自适应衰落卡尔曼滤波（AFKF）设计了一种机械手动态视觉跟踪控制系统。利用残差协方差估计计算遗忘因子，自动调整图像观测数据的权重，提高视觉状态估计精度。当目标特征被遮挡时，利用预测的补偿噪声和预序观测序列生成缺失观测序列的预测，确定用于估计缺失视觉状态的遗忘因子。然后，设计了带有投影误差补偿的参数自适应律，实现了摄像机参数不确定情况下的视觉跟踪。最后，在真实机器人平台上进行了轨迹跟踪实验，验证了所提出的状态估计器和跟踪控制器的性能。结果表明，该方法能够准确地实现轨迹遮挡和相机参数不准确情况下的视觉跟踪，提高了机器人机械臂动态视觉跟踪的灵活性。
This paper focuses on the problem of visual tracking of a moving target with the temporary occlusion of image feature, a dynamic visual tracking control system for robot manipulator is developed by using adaptive fading Kalman filter (AFKF). The estimation of the residual covariance is used to compute the forgetting factor to automatically adjust the weight of the image observation data for improving the visual state estimation accuracy. When the target features are occluded, the prediction of missing observation sequence are generated by using the predicted compensation noise and preorder observation sequence to determine the forgetting factor for estimating the missing visual states. Then, a parameter adaptive law with projection error compensation is designed to realize the visual tracking with uncertain camera parameters. Finally, the trajectory tracking experiments based on a real robot platform is carried out to verify the performance of the proposed state estimator and tracking controller. The results show that the proposed method can accurately realize the visual tracking with the occluded trajectory and inaccurate camera parameters, which improves the flexibility of dynamic visual tracking of robot manipulator.
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