Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. Over the last two decades, LiDAR scanners have become the standard sensor for robot localization and mapping. This article aims to provide an overview of recent progress and advancements in LiDAR-based global localization. We begin by formulating the problem and exploring the application scope. We then present a review of the methodology, including recent advancements in several topics, such as maps, descriptor extraction, and cross-robot localization. The contents of the article are organized under three themes. The first theme concerns the combination of global place retrieval and local pose estimation. The second theme is upgrading single-shot measurements to sequential ones for sequential global localization. Finally, the third theme focuses on extending single-robot global localization to cross-robot localization in multi-robot systems. We conclude the survey with a discussion of open challenges and promising directions in global LiDAR localization. To our best knowledge, this is the first comprehensive survey on global LiDAR localization for mobile robots.

了解自身姿态是所有移动机器人应用的关键。因此，姿态估计是移动机器人核心功能的一部分。在过去的二十年中，激光雷达扫描仪已成为机器人定位和测绘的标准传感器。本文旨在概述基于激光雷达的全球定位的最新进展和进展。我们首先提出问题并探索应用范围。然后，我们对该方法进行回顾，包括地图、描述符提取和跨机器人定位等多个主题的最新进展。本文的内容分为三个主题。第一个主题涉及全局位置检索和局部姿态估计的结合。第二个主题是将单次测量升级为连续测量，以实现连续的全局定位。最后，第三个主题侧重于将单机器人全局定位扩展到多机器人系统中的跨机器人定位。我们在调查结束时讨论了全球激光雷达本地化的开放挑战和有希望的方向。据我们所知，这是首次针对移动机器人全球激光雷达定位的全面调查。