Absolute Visual Localization for Unmanned Aerial Vehicles

Erscheinungsjahr 2027
ISBN: 978-981-9258-73-4
Verlag: Springer

Buch, Englisch, 94 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Intelligent Perception and Information Processing

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.

Zielgruppe

Graduate

Autoren/Hrsg.

Liu, Ganchao

Li, Zhengxi

Bi, Cheng

Yuan, Yuan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

Chapter 1 Introduction.- Chapter 2 Background: UAV Localization Foundations.- Chapter 3 Taxonomy of Absolute Visual Localization Methods.- Chapter 4 Fine-Grained Matching-Based Localization.- Chapter 5 Retrieval-based Absolute Visual Localization.- Chapter 6 Hybrid Retrieval–Matching Methods.- Chapter 7 Cross-View Challenges, Datasets and Evaluation.- Chapter 8 System Integration and Future Directions.- Chapter 9 Overall Summary and Outlook.

Über Autor(innen)

Dr. Ganchao Liu received the Ph.D. degree from Xidian University, Xi’an, China, in 2016. He is currently an Associate Professor with the School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an. His interests include image processing and pattern recognition.

Zhengxi Li received the bachelor’s degree from Xi’an University of Technology, School of Computer Science and Engineering, China, in 2023, and the master’s degree from Northwestern Polytechnical University, Xi’an, China, in 2026. He is currently a researcher with the Changjiang Water Resources Commission. His research interests include image matching and visual localization.

Cheng Bi received the bachelor’s degree from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2021, and the master’s degree from Northwestern Polytechnical University, Xi’an, China, in 2024. He is currently pursuing the Ph.D. degree with the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China. His main research interests are pattern recognition and image processing.

Dr. Yuan Yuan is currently a Full Professor with the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China. She has authored or coauthored over 150 articles, including about 100 in reputable journals, such as IEEE TRANSACTIONS AND PATTERN RECOGNITION, as well as conference papers in CVPR, BMVC, ICIP, and ICASSP. Her current research interests include visual information processing and image/video content analysis.