Fine-Tuning DINOv2 for Terrain Understanding using RUGD

Overview

This project focuses on adapting a self-supervised Vision Transformer (DINOv2) for terrain understanding in unstructured outdoor environments using the RUGD dataset.

While DINOv2 provides strong general-purpose visual representations, real-world robotics applications require domain adaptation to:

• Terrain-specific textures
• Traversability patterns
• Natural environmental variability

This work fine-tunes DINOv2 to improve feature sensitivity toward ground-level semantic distinctions relevant to robotics and navigation systems.

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Objective

To bridge the gap between:

Foundation Vision Models
and
Terrain-aware perception for real-world deployment

By adapting DINOv2 embeddings for:

• Outdoor ground scene understanding
• Unstructured terrain classification
• Robust visual representation learning

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Dataset

RUGD (Robot Unstructured Ground Driving Dataset)

Used for:

• Ground-level scene diversity
• Natural terrain variability
• Robotics-relevant visual semantics

Includes terrain categories such as:

• Dirt
• Grass
• Gravel
• Asphalt
• Vegetation
• Obstacles

This enables learning of representations aligned with real-world navigation contexts.

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Methodology

1. Feature Extraction

• Leveraged pretrained DINOv2 Vision Transformer backbone
• Extracted patch-level embeddings from terrain images

2. Fine-Tuning Strategy

• Adapted representation layers to RUGD-specific terrain patterns
• Improved discriminability across terrain classes
• Preserved foundational visual generalization

3. Supervised Adaptation

• Trained classification head on extracted features
• Optimized model for terrain-aware semantic separation

4. Evaluation

• Assessed class-level performance
• Observed improved contextual sensitivity to ground textures

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Key Contributions

• Adapted a foundation vision model for robotics terrain perception
• Enabled domain-specific representation learning without full model retraining
• Demonstrated applicability of self-supervised models in real-world robotic sensing tasks

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Applications

This approach supports:

• Vision-based navigation
• Autonomous mobility
• Terrain classification systems
• Cross-domain robustness studies

Especially relevant for:

• Robotics
• Autonomous systems
• Field AI deployments

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Tech Stack

• DINOv2
• PyTorch
• Vision Transformers
• RUGD Dataset

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Future Work

• Integration with mobility or navigation pipelines
• Robustness testing across varied environmental conditions
• Transferability analysis to unseen terrains