Utkarsh-Mishra444/Sparsely-Grounded-Long-Range-Navigation
AgentNav: Zero-shot sparsely grounded long-range visual navigation in real-world cities using Multimodal Large Language Models (MLLMs).
This project helps urban planning researchers and autonomous navigation system designers understand how AI agents can navigate complex city environments using only visual cues. It takes street-level images from intersections and a destination, then outputs a sequence of navigation decisions and an estimated path, acting like a human driver or pedestrian navigating without GPS or maps. It's designed for those who need to simulate or evaluate advanced visual navigation capabilities in diverse real-world urban settings.
Use this if you are exploring advanced AI techniques for self-localization and pathfinding in real-world urban environments without relying on GPS, maps, or explicit instructions.
Not ideal if you need a simple, ready-to-deploy GPS-based navigation system or a tool for navigating with pre-defined maps and landmarks.
Stars
8
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2
Language
Python
License
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Category
Last pushed
Mar 17, 2026
Commits (30d)
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