In the fields of autonomous driving, augmented reality, and intelligent mobile robots, visual relocalization is a crucial fundamental issue. It refers to the issue of determining the position and attitude in an existing prior map according to the data captured in real time by visual sensors. In the last decades, visual relocalization has received extensive attention, and numerous kinds of prior map construction methods and visual relocalization methods have come to the fore. These efforts vary considerably and cover a wide scope, but technical overviews and summaries are still unavailable. Therefore, a survey of the field of visual relocalization is valuable both theoretically and practically. This study tries to construct a unified blueprint for visual relocalization methods and summarize related studies from the perspective of image data querying from large-scale map databases. This study surveys various types of construction methods for map databases and different feature matching, relocalization, and pose calculation approaches. It then summarizes the current mainstream datasets for visual relocalization and finally analyzes the challenges ahead and the potential development directions of visual relocalization.