  Fully autonomous mobile robots have a multitude of potential applications,
but guaranteeing robust navigation performance remains an open research
problem. For many tasks such as repeated infrastructure inspection, item
delivery, or inventory transport, a route repeating capability can be
sufficient and offers potential practical advantages over a full navigation
stack. Previous teach and repeat research has achieved high performance in
difficult conditions predominantly by using sophisticated, expensive sensors,
and has often had high computational requirements. Biological systems, such as
small animals and insects like seeing ants, offer a proof of concept that
robust and generalisable navigation can be achieved with extremely limited
visual systems and computing power. In this work we create a novel asynchronous
formulation for teach and repeat navigation that fully utilises odometry
information, paired with a correction signal driven by much more
computationally lightweight visual processing than is typically required. This
correction signal is also decoupled from the robot's motor control, allowing
its rate to be modulated by the available computing capacity. We evaluate this
approach with extensive experimentation on two different robotic platforms, the
Consequential Robotics Miro and the Clearpath Jackal robots, across navigation
trials totalling more than 6000 metres in a range of challenging indoor and
outdoor environments. Our approach continues to succeed when multiple
state-of-the-art systems fail due to low resolution images, unreliable
odometry, or lighting change, while requiring significantly less compute. We
also - for the first time - demonstrate versatile cross-platform teach and
repeat without changing parameters, in which we learn to navigate a route with
one robot and repeat that route using a completely different robot.
