Visual Servoing when Visual Information is Missing: Experimental Comparison of Visual Feature Prediction Schemes

Nicolas Cazy 1 Pierre-Brice Wieber 2 Paolo Robuffo Giordano 1 François Chaumette 1
1 Lagadic - Visual servoing in robotics, computer vision, and augmented reality
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 BIPOP - Modelling, Simulation, Control and Optimization of Non-Smooth Dynamical Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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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Nicolas Cazy, Pierre-Brice Wieber, Paolo Robuffo Giordano, François Chaumette. Visual Servoing when Visual Information is Missing: Experimental Comparison of Visual Feature Prediction Schemes. ICRA'15 - IEEE International Conference on Robotics and Automation, May 2015, Seattle, United States. pp.6031-6036, ⟨10.1109/ICRA.2015.7140045⟩. ⟨hal-01121632⟩

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