The World Outside, the Motion Within: Holistic Spatial Intelligence for UAVs

Spatial intelligence for unmanned aerial vehicles (UAVs) relies on the tight coupling of external environmental perception and internal ego-motion awareness. However, existing models struggle to preserve spatial knowledge across viewpoint changes and to disentangle ego-motion from object motion while capturing how the UAV's motion shapes future observations. To address these challenges, we introduce EnvSelf-UAV, a unified framework that couples external spatial perception with internal ego-motion understanding. Specifically, a Dynamic Environment Spatial Memory (DESM) module maintains spatially grounded memory by updating regional appearance and geometry across video frames. An Ego-Motion Dynamics Modeling (EMDM) module infers UAV motion from shared geometric changes across matched regions and uses a latent world model to predict future environmental representations conditioned on this motion. A Question-Guided Memory Injection (QGMI) mechanism then selectively integrates environmental, motion, and predictive evidence into a language decoder for spatial reasoning. Extensive evaluations across three key benchmarks demonstrate that EnvSelf-UAV achieves superior performance, while downstream navigation tasks further validate the effectiveness of the proposed architecture.