I Can See for Miles and Miles: An Extended Field Test of Visual Teach and Repeat 2.0

Autonomous path-following systems based on the Teach and Repeat paradigm allow robots to traverse extensive networks of manually driven paths using on-board sensors. These methods are well suited for applications that involve repeated traversals of constrained paths such as factory floors, orchards, and mines. In order for path-following systems to be viable for these applications they must be able to navigate large distances over long time periods, a challenging task for vision-based systems that are susceptible to appearance change. This paper details Visual Teach and Repeat 2.0, a vision-based path-following system capable of safe, long-term navigation over large-scale networks of connected paths in unstructured, outdoor environments. These tasks are achieved through the use of a suite of novel, multi-experience, vision-based navigation algorithms. We have validated our system experimentally through an eleven-day field test in an untended gravel pit in Sudbury, Canada, where we incrementally built and autonomously traversed a 5 Km network of paths. Over the span of the field test, the robot logged over 140 Km of autonomous driving with an autonomy rate of 99.6%, despite experiencing significant appearance change due to lighting and weather, including driving at night using headlights.