  Estimating terrain traversability in off-road environments requires reasoning
about complex interaction dynamics between the robot and these terrains.
However, it is challenging to create informative labels to learn a model in a
supervised manner for these interactions. We propose a method that learns to
predict traversability costmaps by combining exteroceptive environmental
information with proprioceptive terrain interaction feedback in a
self-supervised manner. Additionally, we propose a novel way of incorporating
robot velocity in the costmap prediction pipeline. We validate our method in
multiple short and large-scale navigation tasks on challenging off-road
terrains using two different large, all-terrain robots. Our short-scale
navigation results show that using our learned costmaps leads to overall
smoother navigation, and provides the robot with a more fine-grained
understanding of the robot-terrain interactions. Our large-scale navigation
trials show that we can reduce the number of interventions by up to 57%
compared to an occupancy-based navigation baseline in challenging off-road
courses ranging from 400 m to 3150 m. Appendix and full experiment videos can
be found in our website: https://mateoguaman.github.io/hdif.
