Visual Servoing when Visual Information is Missing: Experimental Comparison of Visual Feature Prediction Schemes
Abstract
One way to deal with occlusions or loss of tracking of the visual features used for visual servoing tasks is to predict the feature behavior in the image plane when the measurements are missing. Different prediction and correction methods have already been proposed in the literature. The purpose of this paper is to compare and experimentally validate some of these methods for eye-in-hand and eye-to-hand configurations. In particular, we show that a correction based both on the image and the camera/target pose provides the best results.
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