Deep Visual-Geolocalization in Maritime Coastal Environment

In the marine environment, geolocation is crucial to the autonomy of Unmanned Surface vehicles (USVs). Nowadays, it relies mainly on the Global Navigation Satellite System (GNSS), which is indispensable. However, this system can be vulnerable to jamming or spoofing and needs to be compensated to improve overall resilience. Visual-GeoLocalization (VG) is a significant challenge in maritime environments and is necessary when the context does not allow active sensors. This study investigates a real-time visual localization framework based on deep learning designed for coastal navigation using a limited field-of-view camera. Traditional visual localization methods show a decrease in accuracy when the field of view is restricted, a common constraint for thermal imaging and other specialized sensors. To address this, we propose an artificial intelligence-driven approach that relies on horizon-based correlation techniques to estimate USV localization. Our results highlight the feasibility of GNSS-free navigation for USVs, opening up new possibilities for robust and autonomous maritime operations.