  Visual Teach and Repeat (VT\&R) allows an autonomous vehicle to repeat a
previously traversed route without a global positioning system. Existing
implementations of VT\&R typically rely on 3D sensors such as stereo cameras
for mapping and localization, but many mobile robots are equipped with only 2D
monocular vision for tasks such as teleoperated bomb disposal. While
simultaneous localization and mapping (SLAM) algorithms exist that can recover
3D structure and motion from monocular images, the scale ambiguity inherent in
these methods complicates the estimation and control of lateral path-tracking
error, which is essential for achieving high-accuracy path following. In this
paper, we propose a monocular vision pipeline that enables kilometre-scale
route repetition with centimetre-level accuracy by approximating the ground
surface near the vehicle as planar (with some uncertainty) and recovering
absolute scale from the known position and orientation of the camera relative
to the vehicle. This system provides added value to many existing robots by
allowing for high-accuracy autonomous route repetition with a simple software
upgrade and no additional sensors. We validate our system over 4.3 km of
autonomous navigation and demonstrate accuracy on par with the conventional
stereo pipeline, even in highly non-planar terrain.
