Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. In the last two decades, LiDAR scanners have become a standard sensor for robot localization and mapping. This article surveys recent progress and advances in LiDAR-based global localization. We start with the problem formulation and explore the application scope. We then present the methodology review covering various global localization topics, such as maps, descriptor extraction, and consistency checks. The contents are organized under three themes. The first is the combination of global place retrieval and local pose estimation. Then the second theme is upgrading single-shot measurement to sequential ones for sequential global localization. The third theme is extending single-robot global localization to cross-robot localization on multi-robot systems. We end this survey with a discussion of open challenges and promising directions on global lidar localization.

翻译：有关自身外观的知识是所有移动机器人应用的关键。 因此, 显示的估算是移动机器人核心功能的一部分。 在过去20年中, 激光雷达扫描器已成为机器人定位和绘图的标准传感器。 文章调查了以激光雷达为基础的全球本地化的最新进展和进展。 我们从问题配置开始, 并探索应用范围。 然后我们提出方法审查, 涵盖各种全球本地化专题, 如地图、 描述提取和一致性检查。 内容按三个主题排列。 首先是将全球位置检索和本地面貌估计结合起来。 然后第二个主题是将单点测量升级为连续全球本地化的顺序传感器。 第三个主题是将单点全球本地化扩展至多机器人系统的跨机器人本地化。 我们结束这次调查时, 讨论全球激光本地化的公开挑战和有希望的方向。