  Visual Servoing (VS), where images taken from a camera typically attached to
the robot end-effector are used to guide the robot motions, is an important
technique to tackle robotic tasks that require a high level of accuracy. We
propose a new neural network, based on a Siamese architecture, for highly
accurate camera pose estimation. This, in turn, can be used as a final
refinement step following a coarse VS or, if applied in an iterative manner, as
a standalone VS on its own. The key feature of our neural network is that it
outputs the relative pose between any pair of images, and does so with
sub-millimeter accuracy. We show that our network can reduce pose estimation
errors to 0.6 mm in translation and 0.4 degrees in rotation, from initial
errors of 10 mm / 5 degrees if applied once, or of several cm / tens of degrees
if applied iteratively. The network can generalize to similar objects, is
robust against changing lighting conditions, and to partial occlusions (when
used iteratively). The high accuracy achieved enables tackling low-tolerance
assembly tasks downstream: using our network, an industrial robot can achieve
97.5% success rate on a VGA-connector insertion task without any force sensing
mechanism.
