Adaptive filtering for pose estimation in visual servoing

The extended Kalman filter has been shown to produce accurate pose estimates for visual servoing, assuming that the dynamic noise covariance is known prior to application. Poor estimation of the dynamic noise covariance matrix, Q, can lead to large tracking error or divergence. This paper discusses the use of an adaptive filtering technique to update Q. This provides robust object tracking with unknown trajectory for a visual servoing system with little increase in computational cost. Furthermore, an approximation to a maximum likelihood method with a limited memory filter is proposed, for a time-efficient pose-based visual servoing system.