GA-Nav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments

• Published in: IEEE robotics and automation letters Vol. 7; no. 3; pp. 1 - 8
• Main Authors: Guan, Tianrui, Kothandaraman, Divya, Chandra, Rohan, Sathyamoorthy, Adarsh Jagan, Weerakoon, Kasun, Manocha, Dinesh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AI-Based Methods; AI-Enabled Robotics; Algorithms; Color imagery; Computer architecture; Computer networks; Datasets; Deep Learning for Visual Perception; Deep Learning Methods; Feature extraction; Image segmentation; Machine learning; Navigation; Robots; Semantics; Spatial resolution; Terrain; Transformers; Vision-Based Navigation
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2022.3187278

We propose GA-Nav, a novel group-wise attention mechanism to identify safe and navigable regions in unstructured environments from RGB images. Our group-wise attention method extracts multi-scale features from each type of terrain independently and classifies terrains based on their navigability levels using coarse-grained semantic segmentation. Our novel loss can be embedded within any backbone network to explicitly focus on the different groups' features, at a 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 GA-Nav achieves the state-of-the-art performance on RUGD and RELLIS-3D datasets. We interface GA-Nav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains. We integrate our GA-Nav-based navigation algorithm with ClearPath Jackal and Husky robots, and observe an improvement in terms of navigation success rate and better trajectory selections. Code, videos, and a full technical report are available at https://gamma.umd.edu/offroad/ .