| A methodology to estimate the state of a moving marine vehicle, defined by its position, velocity and heading, from an unmanned surface vehicle (USV), also in motion, using a stereo vision-based system, is presented in this work, in support of following a target vehicle using an USV.;The approach consists of defining a systematic process which encompasses stereo vision ranging, object detection and tracking, and tracking-error minimization. These errors are mainly due to image quantization limitations and pixel miscorrespondences in the stereo pixel-matching process. While more sophisticated matching algorithms may lead to a better depth reconstruction of the scene, at a high computational cost, simple matching algorithms perform faster at the expense of larger errors in depth measurement. This method consists of combining a simple stereo matching algorithm, along with a predictive-corrective approach based on the implementation of an Extended Kalman Filter (EKF), using suitable choices of probabilistic models representing the motion of the target vehicle and the stereo measurements, in order to minimize the tracking errors related to the stereo measurements, and therefore, improve the accuracy of the state estimation of the vehicle.;Results from simulations and a real-time implementation, reveal the effectiveness of the system to compute accurate estimations regarding the state of the target vehicle over non-compliant trajectories subjected to a variety of motion conditions. |
