To address the critical need for autonomous navigation of low-altitude UAVs (unmanned aerial vehicles) in GNSS (global navigation satellite system)-denied environments, a comprehensive survey was presented on absolute visual localization techniques based on a "retrieval-matching-pose estimation" framework. Key challenges inherent to low-altitude UAV observations—including significant imaging disparities, scale variations, and object occlusions—were analyzed, thereby elucidating the advantages of this hierarchical framework for large-scale, long-endurance localization tasks. Subsequently, the technological evolution and state-of-the-art advancements across three core components (cross-view image retrieval, pixel-level feature matching, and UAV pose estimation) were systematically reviewed, tracing the progression from traditional handcrafted features to deep learning paradigms. Finally, considering the deployment requirements of onboard edge computing platforms, the limitations of existing technologies were discussed, and promising future research directions were outlined. This survey is intended to serve as a valuable reference for both research and practical applications in absolute visual localization for low-altitude UAVs.