Aerial drone navigation improves finding outdoor targets with dual belief maps
Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation
RoboticsArtificial Intelligence
Summary
Finding specific objects outdoors with drones is hard because their camera views can be unclear or change quickly. The authors created a system called AeroBelief that helps drones remember where they might find the target by building two layers of information: one that guesses where things could be, and another that confirms actual sightings. This lets the drone explore intelligently and focus on the right areas without getting confused by brief or noisy observations. Their method performed best on a test that measures how well drones find outdoor objects.
What this means in practice
- •For drone navigation engineers: Design UAV navigation systems that maintain persistent spatial memory for locating outdoor targets described in natural language.
- •For outdoor search and rescue teams: Improve UAV-assisted search tasks by enabling drones to better explore and confirm target locations with fewer false leads.
Authors
Jianqiang Xiao, Xiang Deng, Yuexuan Sun, Yanjin Wu, Wenbiao Yan, Liqiang Nie
Abstract
Aerial Object Goal Navigation (ObjectNav) requires an unmanned aerial vehicle (UAV) to locate a described target in an unknown outdoor environment using onboard visual observations. Vision-language models (VLMs) can interpret open-ended target descriptions and visual observations, but their frame-level outputs are often noisy, sparse, and spatially transient. We propose AeroBelief, a dual-layer semantic-spatial belief mapping framework that transforms transient VLM observations into persistent spatial guidance. It separates broad contextual plausibility from target-specific evidence: an intuition layer accumulates scene-level semantic cues for exploration, while an evidence layer preserves qualified target-specific observations for approach and confirmation. Evidence-gated fusion combines the two layers into spatial belief hotspots. We further introduce object-conditioned visual reasoning with conservative evidence qualification to improve observation reliability before spatial accumulation. In parallel, egocentric regional guidance converts quadtree coverage into UAV-centered, yaw-aligned directional proposals and stabilizes them through temporal commitment. Its regional scoring is independent of semantic belief values, maintaining exploration pressure and reducing repeated low-gain search. Experiments on the UAV-ON benchmark show that AeroBelief achieves the best reported overall SR, OSR, and SPL among the compared methods, reaching 21.61%, 35.57%, and 10.62, respectively. These results support the effectiveness of persistent semantic-spatial belief, conservative evidence qualification, and temporally stable regional guidance for aerial ObjectNav.