  Natural environments such as forests and grasslands are challenging for
robotic navigation because of the false perception of rigid obstacles from high
grass, twigs, or bushes. In this work, we propose Wild Visual Navigation (WVN),
an online self-supervised learning system for traversability estimation which
uses only vision. The system is able to continuously adapt from a short human
demonstration in the field. It leverages high-dimensional features from
self-supervised visual transformer models, with an online scheme for
supervision generation that runs in real-time on the robot. We demonstrate the
advantages of our approach with experiments and ablation studies in challenging
environments in forests, parks, and grasslands. Our system is able to bootstrap
the traversable terrain segmentation in less than 5 min of in-field training
time, enabling the robot to navigate in complex outdoor terrains - negotiating
obstacles in high grass as well as a 1.4 km footpath following. While our
experiments were executed with a quadruped robot, ANYmal, the approach
presented can generalize to any ground robot.
