  In dynamic and cramped industrial environments, achieving reliable Visual
Teach and Repeat (VT&R) with a single-camera is challenging. In this work, we
develop a robust method for non-synchronized multi-camera VT&R. Our
contribution are expected Camera Performance Models (CPM) which evaluate the
camera streams from the teach step to determine the most informative one for
localization during the repeat step. By actively selecting the most suitable
camera for localization, we are able to successfully complete missions when one
of the cameras is occluded, faces into feature poor locations or if the
environment has changed. Furthermore, we explore the specific challenges of
achieving VT&R on a dynamic quadruped robot, ANYmal. The camera does not follow
a linear path (due to the walking gait and holonomicity) such that precise
path-following cannot be achieved. Our experiments feature forward and backward
facing stereo cameras showing VT&R performance in cluttered indoor and outdoor
scenarios. We compared the trajectories the robot executed during the repeat
steps demonstrating typical tracking precision of less than 10cm on average.
With a view towards omni-directional localization, we show how the approach
generalizes to four cameras in simulation. Video: https://youtu.be/iAY0lyjAnqY
