Visual teach and generalise (VTAG) - Exploiting perceptual aliasing for scalable autonomous robotic navigation in horticultural environments

Jonathan Cox, Nikolaos Tsagkopoulos, Zdenek Rozsypálek, Tomás Krajník, Elizabeth Sklar, Marc Hanheide

Abstract: Highlights•We extend the classic visual teach & repeat to a novel teach & generalise approach.•Our approach is capable of extracting unique features using a deep learning method.•The approach only uses an uncalibrated monocular camera and wheel odometry.•Successfully traverses several unseen paths having only been taught one similar path.•Our method is entirely developed in ROS enabling fast plug & play implementation.