GeoNav: Empowering MLLMs with dual-scale geospatial reasoning for language-goal aerial navigation

Understand

Language-goal aerial navigation requires UAVs to localize targets in the complex outdoors, such as urban blocks based on textual instructions.

• The indoor methods are often hard to scale to urban scenes due to ambiguous objects, limited visual field, and spatial reasoning.
• In this work, we propose GeoNav, a multi-modal agent for long-range aerial navigation with geospatial awareness.
• GeoNav operates in three phases-landmark navigation, target search, and precise localization-mimicking human coarse-to-fine spatial reasoning patterns.