  Accurate and robust navigation in unstructured environments requires fusing
data from multiple sensors. Such fusion ensures that the robot is better aware
of its surroundings, including areas of the environment that are not
immediately visible but were visible at a different time. To solve this
problem, we propose a method for traversability prediction in challenging
outdoor environments using a sequence of RGB and depth images fused with pose
estimations. Our method, termed WayFASTER (Waypoints-Free Autonomous System for
Traversability with Enhanced Robustness), uses experience data recorded from a
receding horizon estimator to train a self-supervised neural network for
traversability prediction, eliminating the need for heuristics. Our experiments
demonstrate that our method excels at avoiding obstacles, and correctly detects
that traversable terrains, such as tall grass, can be navigable. By using a
sequence of images, WayFASTER significantly enhances the robot's awareness of
its surroundings, enabling it to predict the traversability of terrains that
are not immediately visible. This enhanced awareness contributes to better
navigation performance in environments where such predictive capabilities are
essential.
