Robust 6-DoF Absolute Visual Localization for UAVs via Deep Homography Estimation

Abstract

Accurate absolute localization is essential to uncrewed aerial vehicles (UAVs) operating in low-altitude environments. Global navigation satellite system (GNSS) signals are weak and susceptible to interference, making them unreliable in the case of absolute positioning. Absolute visual localization (AVL), which matches onboard images with geo-referenced satellite maps, provides a viable alternative in GNSS-denied scenarios. However, many existing approaches rely on an assumption of similarity transformation between images to be aligned, which is often violated by nonzero horizontal angles in practice, leading to localization bias and fragility to appearance variations. This article proposes a visual alignment localization method that estimates the full homography between UAV and satellite images and fuses it with incremental motion observations. By jointly modeling orientation and position, the method reduces geometric bias and improves localization robustness under nonnadir viewing conditions. Experiments on real-world UAV datasets demonstrate improved localization accuracy and stability, particularly in flight sequences with nonzero horizontal attitude angles. The source code for our method is available at https://github.com/lizhipro/KF-HomoVAL