We present a self-supervised approach for learning to predict traversable
paths for wheeled mobile robots that require good traction to navigate. Our
algorithm, termed WayFAST (Waypoint Free Autonomous Systems for
Traversability), uses RGB and depth data, along with navigation experience, to
autonomously generate traversable paths in outdoor unstructured environments.
Our key inspiration is that traction can be estimated for rolling robots using
kinodynamic models. Using traction estimates provided by an online receding
horizon estimator, we are able to train a traversability prediction neural
network in a self-supervised manner, without requiring heuristics utilized by
previous methods. We demonstrate the effectiveness of WayFAST through extensive
field testing in varying environments, ranging from sandy dry beaches to forest
canopies and snow covered grass fields. Our results clearly demonstrate that
WayFAST can learn to avoid geometric obstacles as well as untraversable
terrain, such as snow, which would be difficult to avoid with sensors that
provide only geometric data, such as LiDAR. Furthermore, we show that our
training pipeline based on online traction estimates is more data-efficient
than other heuristic-based methods.Comment: Project website with code and videos:
https://mateusgasparino.com/wayfast-traversability-navigation/ Published in
the IEEE Robotics and Automation Letters (RA-L, 2022) Accepted for
presentation in the 2022 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2022