In real-world applications the Perspective-n-Point (PnP) problem should generally be applied in a sequence of images which a set of drift-prone features are tracked over time. In this paper, we consider both the temporal dependency of camera poses and the uncertainty of features for the sequential camera pose estimation. Using the Extended Kalman Filter (EKF), a priori estimate of the camera pose is calculated from the camera motion model and then corrected by minimizing the reprojection error of the reference points. Experimental results, using both simulated and real data, demonstrate that the proposed method improves the robustness of the camera pose estimation, in the presence of noise, compared to the state-of-the-art.
The problem of estimating position and orientation (pose) of an object in real time constitutes an important issue for vision-based control of robots. Many vision-based pose-estimation schemes in robot control rely on an extended Kalman filter (EKF) that requires tuning of filter parameters. To obtain satisfactory results, EKF-based techniques rely on “known” noise statistics, initial object pose, and sufficiently high sampling rates for good approximation of measurement-function linearization. Deviations from such assumptions usually lead to degraded pose estimation during visual servoing. In this paper, a new algorithm, namely iterative adaptive EKF (IAEKF), is proposed by integrating mechanisms for noise adaptation and iterative-measurement linearization. The experimental results are provided to demonstrate the superiority of IAEKF in dealing with erroneous a priori statistics, poor pose initialization, variations in the sampling rate, and trajectory dynamics. Abstract This paper concerns the position-based visual servo control of autonomous robotic manipulators in space. It focuses on the development of a real-time vision-based pose and motion estimation algorithm of a non-cooperative target by photogrammetry and extended Kalman filter for robotic manipulators to perform autonomous capture. Optical flow algorithm is adopted to track the target features in order to improve the image processing efficiency. Then, a close-loop position-based visual servo control strategy is devised to determine the desired pose of the end-effector at the rendezvous point based on the estimated pose and motion of the target. The corresponding desired joint angles of the robotic manipulator in the joint space are derived by the inverse kinematics of the robotic manipulator. The developed algorithm and position-based visual servo control strategy are validated experimentally by a custom built robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the proposed estimation algorithm and control scheme are feasible and effective. 