Altitude-Adaptive UAV-to-Map Image Matching for Aerial Geo-Localization
Altitude-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.
Source: arXiv Robotics — research abstracts · Read original article ↗
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localization threshold distance
100
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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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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
Open source S4
this problem with an altitude-adaptive aerial geo-localization framework. The framework estimates relative altitude from a single image using a frequency-domain representation and regression-as-classification. The predicted altitude-class center defines a scale prior that guides query normalization toward the canonical map scale before full-gallery visual place recognition. A footprint-adaptive margin classifier (FA
Open source S3
itude-derived scale prior improves cross-altitude UAV-to-map image matching in the evaluated regions and camera settings.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org