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.

翻译：自身位姿信息是所有移动机器人应用的关键，因此位姿估计是移动机器人核心功能的一部分。过去二十年中，激光雷达扫描仪已成为机器人定位与建图的标准传感器。本文旨在概述基于激光雷达的全局定位领域的最新进展与突破。我们首先界定问题并探索其应用范围，随后回顾方法论，包括地图构建、描述子提取、跨机器人定位等专题的最新进展。文章内容围绕三个主题展开：第一个主题涉及全局地点检索与局部位姿估计的融合；第二个主题是将单次测量升级为序列测量以实现序贯全局定位；第三个主题则聚焦于将单机器人全局定位扩展至多机器人系统的跨机器人定位。最后，我们通过讨论全局激光雷达定位中的开放挑战与有前景的研究方向来总结本综述。据我们所知，这是首篇关于移动机器人全局激光雷达定位的综合性综述。