  Visual Teach and Repeat (VT&R) has shown relative navigation is a robust and
efficient solution for autonomous vision-based path following in difficult
environments. Adding additional absolute sensors such as Global Navigation
Satellite Systems (GNSS) has the potential to expand the domain of VT&R to
environments where the ability to visually localize is not guaranteed. Our
method of lazy mapping and delaying estimation until a path-tracking error is
needed avoids the need to estimate absolute states. As a result, map
optimization is not required and paths can be driven immediately after being
taught. We validate our approach on a real robot through an experiment in a
joint indoor-outdoor environment comprising 3.5km of autonomous route repeating
across a variety of lighting conditions. We achieve smooth error signals
throughout the runs despite large sections of dropout for each sensor.
