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：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的核心功能之一。近二十年来，激光雷达扫描仪已成为机器人定位与建图的标准传感器。本文综述了基于激光雷达的全局定位方法的最新进展与突破。我们从问题定义出发，探讨其应用范围，进而系统梳理相关方法论，涵盖地图构建、特征描述子提取、一致性校验等全局定位关键主题。内容按三大主题组织：第一主题是全局地点检索与局部位姿估计的融合；第二主题是将单次观测升级为序列化观测，以实现序列化全局定位；第三主题是将单机器人全局定位拓展至多机器人系统的跨机器人定位。最后，我们讨论了全局激光雷达定位领域现存挑战与未来发展方向。