WayFAST: Navigation with Predictive Traversability in the Field

TL;DR

WayFAST is a self-supervised neural network approach that predicts traversable paths for wheeled robots using RGB, depth, and traction estimates, enabling effective navigation in diverse outdoor environments.

Contribution

It introduces a self-supervised training method for traversability prediction using traction estimates, eliminating the need for heuristics and improving data efficiency.

Findings

Successfully navigates diverse terrains including snow and sand

Outperforms heuristic-based methods in data efficiency

Learns to avoid both obstacles and untraversable terrain

Abstract

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.