Altitude-Adaptive UAV-to-Map Image Matching for Aerial Geo-Localization
Overview
This research presents an altitude-adaptive framework for matching downward-looking UAV images to georeferenced map tiles to enable geo-localization when direct positioning signals are unavailable. The method estimates relative altitude from a single image using frequency-domain analysis and regression-as-classification, then uses this estimate to normalize query images to a canonical map scale before performing visual place recognition. A footprint-adaptive margin classifier further adjusts training margins based on ground footprint overlap. Experiments on two synthetic and two real-flight datasets show that altitude adaptation improves average R@1 and R@5 by 50.68 and 47.20 percentage points, respectively, compared to a baseline retrieval pipeline without altitude normalization.
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“MC) adjusts training margins according to footprint overlap. Experiments on two synthetic and two real-flight datasets show that, at the 100 m localization threshold, altitude adaptation improves average R@1 and R@5 by 50.68 and 47.20 percentage points, respectively, for our visual place recognition module compared with the same retrieval pipeline without altitude normalization. These results show that a relative-alt”
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- arXiv Robotics — research abstractsSignalAltitude-Adaptive UAV-to-Map Image Matching for Aerial Geo-Localization
This research presents an altitude-adaptive framework for matching downward-looking UAV images to georeferenced map tiles to enable geo-localization when direct positioning signals are unavailable. The method estimates relative altitude from a single image using frequency-domain analysis and regression-as-classification, then uses this estimate to normalize query images to a canonical map scale before performing visual place recognition. A footprint-adaptive margin classifier further adjusts training margins based on ground footprint overlap. Experiments on two synthetic and two real-flight datasets show that altitude adaptation improves average R@1 and R@5 by 50.68 and 47.20 percentage points, respectively, compared to a baseline retrieval pipeline without altitude normalization.
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