  Visual Teach and Repeat 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 Visual Teach
and Repeat 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.
