  A key challenge in off-road navigation is that even visually similar terrains
or ones from the same semantic class may have substantially different traction
properties. Existing work typically assumes no wheel slip or uses the expected
traction for motion planning, where the predicted trajectories provide a poor
indication of the actual performance if the terrain traction has high
uncertainty. In contrast, this work proposes to analyze terrain traversability
with the empirical distribution of traction parameters in unicycle dynamics,
which can be learned by a neural network in a self-supervised fashion. The
probabilistic traction model leads to two risk-aware cost formulations that
account for the worst-case expected cost and traction. To help the learned
model generalize to unseen environment, terrains with features that lead to
unreliable predictions are detected via a density estimator fit to the trained
network's latent space and avoided via auxiliary penalties during planning.
Simulation results demonstrate that the proposed approach outperforms existing
work that assumes no slip or uses the expected traction in both navigation
success rate and completion time. Furthermore, avoiding terrains with low
density-based confidence score achieves up to 30% improvement in success rate
when the learned traction model is used in a novel environment.
