Monocular Re-Localization (MRL) is a critical component in autonomous applications, estimating 6 degree-of-freedom ego poses w.r.t. the scene map based on monocular images. In recent decades, significant progress has been made in the development of MRL techniques. Numerous algorithms have accomplished extraordinary success in terms of localization accuracy and robustness. In MRL, scene maps are represented in various forms, and they determine how MRL methods work and how MRL methods perform. However, to the best of our knowledge, existing surveys do not provide systematic reviews about the relationship between MRL solutions and their used scene map representation. This survey fills the gap by comprehensively reviewing MRL methods from such a perspective, promoting further research. 1) We commence by delving into the problem definition of MRL, exploring current challenges, and comparing ours with existing surveys. 2) Many well-known MRL methods are categorized and reviewed into five classes according to the representation forms of utilized map, i.e., geo-tagged frames, visual landmarks, point clouds, vectorized semantic map, and neural network-based map. 3) To quantitatively and fairly compare MRL methods with various map, we introduce some public datasets and provide the performances of some state-of-the-art MRL methods. The strengths and weakness of MRL methods with different map are analyzed. 4) We finally introduce some topics of interest in this field and give personal opinions. This survey can serve as a valuable referenced materials for MRL, and a continuously updated summary of this survey is publicly available to the community at: https://github.com/jinyummiao/map-in-mono-reloc.

翻译：单目重定位（MRL）是自主应用中的关键组成部分，其基于单目图像估计相对于场景地图的六自由度自车位姿。近年来，MRL技术取得了显著进展，大量算法在定位精度和鲁棒性方面取得了卓越成就。在MRL中，场景地图以多种形式表示，这些表示形式决定了MRL方法的工作方式及性能表现。然而，据我们所知，现有综述尚未系统梳理MRL解决方案与其所用场景地图表示之间的关系。本综述从这一视角出发，全面回顾MRL方法，填补研究空白，推动后续发展：1）首先深入探讨MRL的问题定义，分析当前挑战，并与现有综述进行比较；2）根据所用地图的表示形式（即地理标记帧、视觉地标、点云、向量化语义地图及基于神经网络的地图），将众多知名MRL方法分为五类进行综述；3）为定量公平比较不同地图表示的MRL方法，介绍若干公开数据集，并展示最新MRL方法的性能，分析不同地图表示方法的优劣；4）最后介绍该领域的研究热点并给出个人见解。本综述可作为MRL领域的重要参考资料，其持续更新的总结版本已公开于社区：https://github.com/jinyummiao/map-in-mono-reloc。