GANav: Group-wise Attention for Classifying Navigable Regions in Unstructured Outdoor Environments


We present a new learning-based method for identifying safe and navigable regions in off-road terrains and unstructured environments from RGB images. Our approach consists of classifying groups of terrains based on their navigability levels using coarse-grained semantic segmentation. We propose a bottleneck transformer-based deep neural network architecture that uses a novel group-wise attention mechanism to distinguish between navigability levels of different terrains. Our group-wise attention heads enable the network to explicitly focus on the different groups and improve the accuracy. We show through extensive evaluations on the RUGD and RELLIS-3D datasets that our learning algorithm improves visual perception accuracy in off-road terrains for navigation. We compare our approach with prior work on these datasets and achieve an improvement over the state-of-the-art mIoU by 6.74-39.1% on RUGD and 3.82-10.64% on RELLIS-3D. In addition, we deploy our method on a Clearpath Jackal robot. Our approach improves the performance of the navigation algorithm in terms of average progress towards the goal by 54.73% and the false positives in terms of forbidden region by 29.96%. Supplementary materials including code, videos, and a full technical report are available at
Paper Code Dataset Tech Report
GANav Github RUGD/RELLIS-3D Tech Report


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      title={GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments}, 
      author={Tianrui Guan and Divya Kothandaraman and Rohan Chandra and Adarsh Jagan Sathyamoorthy and Dinesh Manocha},