# GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments

arXiv: https://arxiv.org/abs/2103.04233 · HTML: https://mlsanity.com/paper/2103.04233/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 7 Mar 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Meta Pseudo Labels](https://mlsanity.com/paper/2003.10580/) (2020)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [Bottleneck Transformers for Visual Recognition](https://mlsanity.com/paper/2101.11605/) (2021)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [Fast-SCNN: Fast Semantic Segmentation Network](https://mlsanity.com/paper/1902.04502/) (2019)
- [Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes](https://mlsanity.com/paper/1708.01566/) (2017)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [CGNet: A Light-weight Context Guided Network for Semantic Segmentation](https://mlsanity.com/paper/1811.08201/) (2018)
- [FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation](https://mlsanity.com/paper/1903.11816/) (2019)

## Built on by (most cited first, 30 of 51)

- [Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos](https://mlsanity.com/paper/2501.04001/) (2025)
- [Learning Off-Road Terrain Traversability with Self-Supervisions Only](https://mlsanity.com/paper/2305.18896/) (2023)
- [EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy](https://mlsanity.com/paper/2311.06234/) (2023)
- [Excavating in the Wild: The GOOSE-Ex Dataset for Semantic Segmentation](https://mlsanity.com/paper/2409.18788/) (2024)
- [STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience](https://mlsanity.com/paper/2309.15302/) (2023)
- [MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited Memory](https://mlsanity.com/paper/2310.16898/) (2023)
- [Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments](https://mlsanity.com/paper/2409.20445/) (2024)
- [VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain](https://mlsanity.com/paper/2209.07725/) (2022)
- [Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models](https://mlsanity.com/paper/2310.08873/) (2023)
- [Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2304.07314/) (2023)
- [CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning](https://mlsanity.com/paper/2308.06594/) (2023)
- [Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning](https://mlsanity.com/paper/2309.09912/) (2023)
- [SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces](https://mlsanity.com/paper/2303.05584/) (2023)
- [Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception](https://mlsanity.com/paper/2306.14601/) (2023)
- [UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation](https://mlsanity.com/paper/2403.02642/) (2024)
- [Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy](https://mlsanity.com/paper/2406.02989/) (2024)
- [PACER: Preference-conditioned All-terrain Costmap Generation](https://mlsanity.com/paper/2410.23488/) (2024)
- [Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping](https://mlsanity.com/paper/2403.11876/) (2024)
- [Few-shot Semantic Learning for Robust Multi-Biome 3D Semantic Mapping in Off-Road Environments](https://mlsanity.com/paper/2411.06632/) (2024)
- [LabelGS: Label-Aware 3D Gaussian Splatting for 3D Scene Segmentation](https://mlsanity.com/paper/2508.19699/) (2025)
- [An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation](https://mlsanity.com/paper/2202.09002/) (2022)
- [AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales](https://mlsanity.com/paper/2404.03187/) (2024)
- [TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation](https://mlsanity.com/paper/2404.15256/) (2024)
- [Words to Wheels: Vision-Based Autonomous Driving Understanding Human Language Instructions Using Foundation Models](https://mlsanity.com/paper/2410.10577/) (2024)
- [Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation](https://mlsanity.com/paper/2505.22805/) (2025)
- [AnyTraverse: An off-road traversability framework with VLM and human operator in the loop](https://mlsanity.com/paper/2506.16826/) (2025)
- [AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2507.17957/) (2025)
- [From General Vision to Reliable Traversability Estimation: Adapting Vision Foundation Models for Unstructured Outdoor Environments](https://mlsanity.com/paper/2605.29565/) (2026)
- [Vision based Crop Row Navigation under Varying Field Conditions in Arable Fields](https://mlsanity.com/paper/2209.14003/) (2022)
- [TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation](https://mlsanity.com/paper/2406.01395/) (2024)

## Abstract (arXiv)

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/.