Georeferenced UAV Localization in Mountainous Terrain under GNSS-Denied Conditions

In Global Navigation Satellite System (GNSS)-denied environments, Unmanned Aerial Vehicles (UAVs) relying on Vision-Based Navigation (VBN) in high-altitude, moun-tainous terrain face severe challenges due to geometric distortions in aerial imagery. This paper proposes a georeferenced localization framework that integrates orthorec-tified aerial imagery with Scene Matching (SM) to achieve robust positioning. The method employs a camera projection model combined with Digital Elevation Model (DEM) to orthorectify UAV images, thereby mitigating distortions from central projec-tion and terrain relief. Pre-processing steps—including illumination normalization, lens distortion correction, rotational alignment, and resolution adjustment—enhance consistency with reference orthophoto maps, after which template matching is per-formed using Normalized Cross-Correlation (NCC). Sensor fusion is achieved through Extended Kalman Filter (EKF) incorporating Inertial Navigation System (INS), GNSS (when available), barometric altimeter, and SM outputs, with sub-modules for hori-zontal, vertical, and altimeter error estimation. The framework was validated through flight tests with an aircraft over 45 km trajectories at altitudes of 2.5 km and 3.5 km in mountainous terrain. Results demonstrate the orthorectification improves image simi-larity and significantly reduces localization error, yielding lower 2D RMSE compared to conventional rectification. The proposed approach enhances VBN by mitigating terrain-induced distortions, providing a practical solution for UAV localization in GNSS-denied scenarios.