GA-Nav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments
Tianrui Guan, Divya Kothandaraman, Rohan Chandra, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha
Abstract
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.
BibTeX
@inproceedings{ral2022_ganavefficientte,
title = {GA-Nav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments},
author = {Tianrui Guan and Divya Kothandaraman and Rohan Chandra and Adarsh Jagan Sathyamoorthy and Kasun Weerakoon and Dinesh Manocha},
booktitle = {RA-L 2022},
year = {2022}
}