{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ganav-group-wise-attention-network-for","title":"GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments","arxiv_id":"2103.04233","date":"2021-03-07","proceeding":null,"authors":["Tianrui Guan","Divya Kothandaraman","Rohan Chandra","Adarsh Jagan Sathyamoorthy","Kasun Weerakoon","Dinesh Manocha"],"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/.","url_abs":"https://arxiv.org/abs/2103.04233v5","url_pdf":"https://arxiv.org/pdf/2103.04233v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ganav-group-wise-attention-network-for","repo_url":"https://github.com/rayguan97/GANav-offroad","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-rellis-3d-dataset","task":"Semantic Segmentation","dataset":"RELLIS-3D Dataset","model":"GA-Nav","rank_in_archive_order":2,"of":5,"metrics":{"Mean IoU (class)":"74.44"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-rugd","task":"Semantic Segmentation","dataset":"RUGD","model":"GA-Nav","rank_in_archive_order":1,"of":1,"metrics":{"AIOU":"95.66","mIoU":"89.08"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2103.04233","atlas_url":"https://app.syntology.ai/?focus=2103.04233","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}