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

• URL: http://arxiv.org/abs/2209.10788v1
• Date: Thu, 22 Sep 2022 05:18:35 GMT
• Title: How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
• Authors: Mateo Guaman Castro, Samuel Triest, Wenshan Wang, Jason M. Gregory, Felix Sanchez, John G. Rogers III, Sebastian Scherer
• Abstract summary: Estimating terrain traversability in off-road environments requires reasoning about complex interaction dynamics between the robot and these terrains. We propose a method that learns to predict traversability costmaps by combining exteroceptive environmental information with proprioceptive terrain interaction feedback.
• Score: 7.305104984234086
• License: http://creativecommons.org/licenses/by/4.0/
• 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 build an accurate physics model, or 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 a large, autonomous all-terrain vehicle (ATV) on challenging off-road terrains, and demonstrate ease of integration on a separate large ground robot. 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 interactions between the robot and different terrain types, such as grass and gravel. 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.

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