LiDAR-based place recognition (LPR) plays a pivotal role in autonomous driving, which assists Simultaneous Localization and Mapping (SLAM) systems in reducing accumulated errors and achieving reliable localization. However, existing reviews predominantly concentrate on visual place recognition (VPR) methods. Despite notable advancements in LPR in recent years, there is yet a systematic review dedicated to this field to the best of our knowledge. This paper bridges the gap by providing a comprehensive review of place recognition methods employing LiDAR sensors, thus facilitating and encouraging further research. We commence by delving into the problem formulation of place recognition and exploring existing challenges, describing relations to previous surveys. Subsequently, we conduct an in-depth review of related research, which offers detailed classifications, strengths and weaknesses, and architectures. Finally, we summarize existing datasets, commonly used evaluation metrics, and comprehensive evaluation results from various methods on public datasets. This paper can serve as a valuable tutorial for newcomers entering the realm of place recognition and researchers interested in long-term robot localization. We pledge to maintain an up-to-date project on our website https://github.com/ShiPC-AI/LPR-Survey.

翻译：基于激光雷达的场所识别（LPR）在自动驾驶中发挥着关键作用，它有助于同步定位与地图构建（SLAM）系统减少累积误差并实现可靠定位。然而，现有综述主要聚焦于视觉场所识别（VPR）方法。尽管近年来LPR取得了显著进展，但据我们所知，目前尚缺乏专门针对该领域的系统性综述。本文通过全面梳理基于LiDAR传感器的场所识别方法弥合了这一空白，从而促进并鼓励进一步的研究。我们首先深入探讨场所识别的问题建模并分析现有挑战，阐述与以往综述的关系。随后，我们对相关研究进行深入综述，提供详细的分类、优缺点以及架构分析。最后，我们总结了现有数据集、常用评估指标以及各方法在公开数据集上的综合评估结果。本文可作为该领域新入门者及关注长期机器人定位的研究人员的有价值指南。我们承诺在我们的网站https://github.com/ShiPC-AI/LPR-Survey上保持该项目更新。