  Traversability estimation in off-road terrains is an essential procedure for
autonomous navigation. However, creating reliable labels for complex
interactions between the robot and the surface is still a challenging problem
in learning-based costmap generation. To address this, we propose a method that
predicts traversability costmaps by leveraging both visual and geometric
information of the environment. To quantify the surface properties like
roughness and bumpiness, we introduce a novel way of risk-aware labelling with
proprioceptive information for network training. We validate our method in
costmap prediction and navigation tasks for complex off-road scenarios. Our
results demonstrate that our costmap prediction method excels in terms of
average accuracy and MSE. The navigation results indicate that using our
learned costmaps leads to safer and smoother driving, outperforming previous
methods in terms of the highest success rate, lowest normalized trajectory
length, lowest time cost, and highest mean stability across two scenarios.
