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- W2788822937 abstract "We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF positioning tasks by visual servoing. A convolutional neural network is fine-tuned to estimate the relative pose between the current and desired images and a pose-based visual servoing control law is considered to reach the desired pose. The paper describes how to efficiently and automatically create a dataset used to train the network. We show that this enables the robust handling of various perturbations (occlusions and lighting variations). We then propose the training of a scene-agnostic network by feeding in both the desired and current images into a deep network. The method is validated on a 6 DOF robot." @default.
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- W2788822937 date "2018-05-01" @default.
- W2788822937 modified "2023-10-09" @default.
- W2788822937 title "Training Deep Neural Networks for Visual Servoing" @default.
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- W2788822937 doi "https://doi.org/10.1109/icra.2018.8461068" @default.
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