Stereo mapping and localization for long-range path following on rough terrain

Visual teach-and-repeat navigation enables long-range rover autonomy without solving the simultaneous localization and mapping problem or requiring an accurate global reconstruction. During a learning phase, the rover is piloted along a route, logging images. After post-processing, the rover is able to repeat the route in either direction any number of times. This paper describes and evaluates the localization algorithm at the core of a teach-and-repeat system that has been tested on over 32 kilometers of autonomous driving in an urban environment and at a planetary analog site in the High Arctic. We show how a stereo visual odometry pipeline can be extended to become a mapping and localization system, then evaluate the performance of the algorithm with respect to accuracy, robustness to path-tracking error, and the effects of lighting.