GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments

TL;DR

GANav introduces a group-wise attention mechanism for efficient terrain segmentation, significantly improving navigability classification accuracy in unstructured outdoor environments for robotic navigation.

Contribution

The paper presents a novel group-wise attention loss that enhances backbone networks for terrain segmentation, enabling better focus on features at low spatial resolution.

Findings

Achieves 2.25-39.05% higher mIoU on RUGD dataset

Improves navigation success rate by 10% on real robots

Reduces false positive rate of forbidden regions by 37.79%

Abstract

We propose GANav, a novel group-wise attention mechanism to identify safe and navigable regions in off-road terrains and unstructured environments from RGB images. Our approach classifies terrains based on their navigability levels using coarse-grained semantic segmentation. Our novel group-wise attention loss enables any backbone network to explicitly focus on the different groups' features with low spatial resolution. Our design leads to efficient inference while maintaining a high level of accuracy compared to existing SOTA methods. Our extensive evaluations on the RUGD and RELLIS-3D datasets shows that GANav achieves an improvement over the SOTA mIoU by 2.25-39.05% on RUGD and 5.17-19.06% on RELLIS-3D. We interface GANav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains. We integrate our GANav-based navigation algorithm with ClearPath Jackal and Husky robots, and observe an increase of 10% in terms of success rate, 2-47% in terms of selecting the surface with the best navigability and a decrease of 4.6-13.9% in trajectory roughness. Further, GANav reduces the false positive rate of forbidden regions by 37.79%. Code, videos, and a full technical report are available at https://gamma.umd.edu/offroad/.