Aerial-to-satellite visual localization enables GNSS-denied UAV navigation, but the appearance gap between low-altitude (50–150 m) UAV imagery and nadir satellite tiles makes per-frame Visual Place Recognition (VPR) unreliable. Under perceptual aliasing, high-similarity matches are often geographically inconsistent, so naïve anchoring fails. We introduce NaviLoc, a training-free three-stage trajectory-level estimator that treats VPR as a noisy measurement source and exploits Visual-Inertial Odometry (VIO) as a relative-motion prior. Stage 1 (Global Align) estimates a global SE(2) transform by maximizing an explicit trajectory-level similarity objective. Stage 2 (Refinement) performs sliding-window bounded weighted Procrustes updates. Stage 3 (Smoothing) computes a strictly convex MAP trajectory estimate that fuses VIO displacements with VPR anchors while clamping detected outliers. On a challenging low-altitude rural UAV benchmark, NaviLoc attains 19.5 m Mean Localization Error (MLE)—a 16.0× reduction compared to state-of-the-art localization method AnyLoc-VLAD, and 32.1× compared to raw VIO drift. End-to-end inference runs at 9 FPS on Raspberry Pi 5, enabling real-time embedded deployment.
Shpagin et al. (Thu,) studied this question.
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