autonomous_drone_nav_vision

Overview

A Vision Transformer (ViT) fine-tuned for tactical aerial navigation. This model enables Small Unmanned Aircraft Systems (sUAS) to classify environmental obstacles and identify safe landing zones in real-time using downward and forward-facing RGB cameras.

Model Architecture

The model utilizes a Vision Transformer (ViT-Base) backbone:

• Patch Extraction: Images are divided into $16 \times 16$ fixed-size patches.
• Position Embeddings: Learnable spatial embeddings are added to the patch sequence to retain structural context.
• Attention Mechanism: Global self-attention allows the model to correlate distant visual cues, such as horizon lines and ground markers.

Intended Use

• Obstacle Avoidance: Integrated into flight control stacks for autonomous "sense and avoid" maneuvers.
• Precision Landing: Identifying designated markers or flat terrain for autonomous recovery.
• Search and Rescue: Preliminary screening of aerial footage to identify human-made structures or anomalies.

Limitations

• Low Light: Performance degrades significantly in nighttime or heavy fog conditions without thermal input.
• Motion Blur: Rapid yaw movements at high speeds may cause misclassification due to pixel streaking.
• Scale Invariance: Small objects at extreme altitudes may be missed due to the fixed $224 \times 224$ input resolution.