What if navigation systems could remember routes visually instead of depending entirely on GPS?
Introducing 𝗩𝗫𝗡-𝗥𝗔𝗠𝗡𝗲𝘁 (𝗩𝗶𝘀𝗶𝗼𝗻𝗫 𝗥𝗼𝘂𝘁𝗶𝗻𝗲 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗠𝗲𝗺𝗼𝗿𝘆 𝗡𝗲𝘁𝘄𝗼𝗿𝗸) — a research-oriented visual route-memory and branch-graph learning architecture for assistive navigation intelligence.
This project explores how repeated routes can be learned directly from route videos using:
• Static visual embeddings
• DTW synchronization
• Shared-path detection
• Graph-based route memory
• LEFT/RIGHT branch divergence learning
• Query-route classification
• Uncertainty handling
• Unknown-route auto-learning
Implemented concepts include:
- EfficientNet visual embeddings
- Dynamic Time Warping (DTW)
- Shared-prefix graph learning
- Divergence detection
- Route-memory classification
- Real-time oriented modular architecture
One of the biggest learnings during this project was understanding how deeply concepts like DSA, graphs, similarity learning, and temporal synchronization connect with real-world AI systems.
GitHub Repository: (https://github.com/AjaySoni-Dev/VXN-RAMNet)
Top comments (0)