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arXiv · 1705.08940

Visual Servoing from Deep Neural Networks

Quentin Bateux·Eric Marchand·Jürgen Leitner·Francois Chaumette·Peter Corke
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Abstract

We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions.A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.

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Quentin Bateux, Eric Marchand, Jürgen Leitner, Francois Chaumette, Peter Corke. 2017-05-24. Visual Servoing from Deep Neural Networks. https://arxiv.org/abs/1705.08940

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