Advances in UAV visual positioning technology based on deep learning

Unmanned Aerial Vehicle (UAV) visual localization is a key technology for enabling autonomous flight in complex environments. Under Global Navigation Satellite System (GNSS)-denied or GNSS-degraded conditions, conventional approaches based on Inertial Measurement Unit (IMU)/Visual Odometry (VO) fusion and handcrafted feature matching often struggle to cope with large viewpoint variations, significant scale changes, and challenging illumination conditions. Recent advances in deep learning have substantially improved the accuracy and robustness of UAV visual localization. This paper presents a comprehensive survey of recent advances in deep learning-based UAV visual localization and systematically reviews the evolution of related research. According to the underlying technical paradigms, existing methods are categorized into three groups: template similarity-based localization, feature descriptor matching-based localization, and end-to-end pose regression-based localization. Furthermore, the key techniques within each category are systematically analyzed, and representative methods are compared in terms of their fundamental principles, technical characteristics, application scenarios, and localization performance, with particular emphasis on localization accuracy, robustness, and computational efficiency. Finally, the major challenges in current research, including incomplete or mismatched maps, large viewpoint variations, adaptation to complex environments, and localization reliability, are discussed. Future research directions, such as multi-sensor fusion, motion continuity constraints, and intelligent autonomous localization, are also highlighted, with the aim of providing useful insights for the further development of UAV visual localization technologies.