For monocular camera-based visual relocalization, the research status and the latest progress are reviewed, and some key methods are introduced. Different from the existing vertical classification frameworks of relocalization methods, this paper proposes an intuitive and unified horizontal classification framework, which is mainly carried out from 3 aspects, including the scene model construction, scene information matching and camera pose solving. The deep-learning-based and geometric-structure-based methods are elaborated in the framework uniformly for the first time. Based on the in-depth performance analysis and visualization results, factors leading to performance bottlenecks and challenges of camera pose estimation are pointed out. Meanwhile, state-of-the-art methods of camera pose estimation are analyzed and summarized. Finally, the development trends of visual relocalization methods in the future are prospected.