How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability

TL;DR

This paper introduces a self-supervised learning approach for off-road terrain traversability estimation, combining environmental and interaction data to improve navigation safety and efficiency on challenging terrains.

Contribution

It presents a novel method that integrates robot velocity into costmap prediction and demonstrates significant improvements in navigation performance and reduced interventions.

Findings

Reduced interventions by up to 57% in large-scale trials

Achieved smoother navigation with learned costmaps

Validated on multiple off-road robots and terrains

Abstract

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.