How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
This addresses the challenge of autonomous navigation in complex off-road environments for robotics, though it appears incremental as it builds on existing costmap and self-supervised learning approaches.
The paper tackles the problem of estimating terrain traversability for off-road vehicles by learning costmaps in a self-supervised manner, reducing interventions by up to 57% compared to a baseline in large-scale navigation trials.
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.