Q. Bateux, E. Marchand, J. Leitner, F. Chaumette, P. Corke. Training Deep Neural Networks for Visual Servoing. In IEEE Int. Conf. on Robotics and Automation, ICRA’18, Brisbane, Australia, May 2018.
We proposed a deep neural network-based method to perform high-precision, robust and real-time 6 DOF positioning tasks by visual servoing. We show that a convolutional neural network can be 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. We also considered how to efficiently and automatically create a dataset used to train the network. We also show that the proposed the training of a scene-agnostic network by feeding in both the desired and current images into a deep network.