Absolute Visual Localization for Unmanned Aerial Vehicles
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Beschreibung
This book provides a systematic survey of unmanned aerial vehicle (UAV) visual positioning, with a primary emphasis on two central technical paths: image retrieval and image matching. These approaches lie at the heart of UAV–satellite cross-view geolocation, enabling accurate absolute positioning when GPS is unavailable or unreliable. We highlight advances in feature-based matching pipelines and deep learning–driven retrieval frameworks, which have become the most active and promising research directions. Other approaches—such as multi-sensor fusion, semantic and geometric modeling, and end-to-end learning frameworks—are briefly introduced to provide context, illustrating how UAV visual positioning is evolving into a broader ecosystem. By framing image retrieval and image matching as the core methods while situating them within the wider methodological landscape, this survey clarifies current progress, identifies open challenges, and points to future research opportunities. The intended audience includes graduate students, researchers, and engineers working on UAV navigation, computer vision, robotics, and geospatial information systems.
This book provides a systematic survey of unmanned aerial vehicle (UAV) visual positioning, with a primary emphasis on two central technical paths: image retrieval and image matching. These approaches lie at the heart of UAV–satellite cross-view geolocation, enabling accurate absolute positioning when GPS is unavailable or unreliable. We highlight advances in feature-based matching pipelines and deep learning–driven retrieval frameworks, which have become the most active and promising research directions. Other approaches—such as multi-sensor fusion, semantic and geometric modeling, and end-to-end learning frameworks—are briefly introduced to provide context, illustrating how UAV visual positioning is evolving into a broader ecosystem. By framing image retrieval and image matching as the core methods while situating them within the wider methodological landscape, this survey clarifies current progress, identifies open challenges, and points to future research opportunities. The intended audience includes graduate students, researchers, and engineers working on UAV navigation, computer vision, robotics, and geospatial information systems.
Bridges retrieval, fine-grained matching, and hybrid localization pipelines for scalable and accurate UAV positioning Covers cross-view challenges, public datasets, evaluation metrics, and system integration for real-world UAV deployment Systematically reviews UAV absolute visual localization from GNSS/IMU foundations to deep learning-based AVL methods
Autor*in
Ganchao Liu
Themen in »Absolute Visual Localization for Unmanned Aerial Vehicles«
Visual Localization GNSS-denied navigation Cross-view Geo-localization (CVGL) Deep Learning Absolute Visual Localization Unmanned Aerial Vehicles (UAV)