Improving image-based visual servoing with reference features filtering

In this paper, Image-Based Visual Servoing (IBVS) is addressed via a new control structure where reference features are previously filtered based on an estimation of these reference features using standard filters such as Kalman Filter (KF) or a combination of a Kalman Filter and a Smoother (KFS). In this sense, one of the key aspects of the paper is to predict feasible reference features for the low-level IBVS controller. Along the paper, we discuss and analyze the improvements introduced with the new control structure in terms of convergence time and reachability, that is, the ability to converge in complex scenarios such as rotations around the camera axis and large displacements. Validations are also provided through real experimentation with an industrial robot of 6 DOF in eye-in-hand configuration.