Visual Servoing from Deep Neural Networks
This addresses robust real-time visual servoing for robotics, but it is incremental as it builds on existing deep learning and control methods.
The paper tackles high-precision 6 DOF visual servoing by fine-tuning a convolutional neural network on a simulated dataset from a single real-world image to estimate relative pose, achieving a positioning error of less than one millimeter in robot experiments.
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.