Wild Visual Navigation: Fast Traversability Learning via Pre-Trained
  Models and Online Self-Supervision
Matías Mattamala Jonas Frey Piotr Libera Nived Chebrolu Georg Martius Cesar Cadena Marco Hutter Maurice Fallon
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 present Wild Visual Navigation (WVN),
an online self-supervised learning system for visual traversability estimation.
The system is able to continuously adapt from a short human demonstration in
the field, only using onboard sensing and computing. One of the key ideas to
achieve this is the use of high-dimensional features from pre-trained
self-supervised models, which implicitly encode semantic information that
massively simplifies the learning task. Further, the development of an online
scheme for supervision generator enables concurrent training and inference of
the learned model in the wild. We demonstrate our approach through diverse
real-world deployments 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, previously unseen
outdoor terrains. Code: https://bit.ly/498b0CV - Project
page:https://bit.ly/3M6nMHH