Robust Visual Servoing based on Novel View Prediction

In this paper we propose a novel technique for robust visual servoing in presence of a large proportion of outliers in image measurements. The method employs robust statistical techniques and novel view prediction for improving the performance. We identify a set of points from initial and reference images and compute the essential matrix relating them. The selected points are predicted from the initial and reference images to the current frame using essential matrices. A function of the difference between the observed and predicted image point measurements is used to identify outliers. This technique is validated with many experiments and compared with other robust methods in a simulation framework.

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