  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/.
