OPsCV: A Robust Framework for Aerial Navigation under Global Positioning System Denied Conditions

Unmanned aerial vehicles (UAVs) rely heavily on the global navigation satellite system (GNSS) for accurate localization. However, GNSS signals are often unavailable or unreliable in contested or cluttered environments. This study presents the optimized pose prediction and cross-view (OPsCV), a robust and adaptable navigation framework that integrates deep inertial odometry with a simulated cross-view geolocalization module through an error-state Kalman filter. The system enables dynamic switching from GNSS-based positioning to a fused solution that combines inertial and vision-based estimates as GNSS signal quality degrades. The framework was evaluated using real UAV flight data under persistent GNSS denial, with results demonstrating reliable pose estimation and improved positioning accuracy compared to the UAV’s internal navigation system. The OPsCV method maintained performance even with sparse cross-view updates, confirming its resilience under conservative operational conditions. These findings highlight the potential of fusing learned inertial measurements with statistical vision-based localization for autonomous aerial navigation in GNSS-denied environments.