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 the recent remarkable progress in LPR, to the best of our knowledge, there is no dedicated systematic review in this area. 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, exploring existing challenges, and 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 field of place recognition and for 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：基于激光雷达的地点识别（LPR）在自动驾驶中起着关键作用，它有助于同步定位与建图（SLAM）系统减少累积误差并实现可靠的定位。然而，现有的综述主要集中于视觉地点识别（VPR）方法。尽管LPR领域近期取得了显著进展，但据我们所知，目前尚无该领域的专门系统性综述。本文通过全面综述采用激光雷达传感器的地点识别方法，填补了这一空白，从而促进和鼓励进一步的研究。我们首先深入探讨地点识别的问题定义，分析现有挑战，并描述与先前综述的关系。随后，我们对相关研究进行了深入回顾，提供了详细的分类、优缺点分析以及架构描述。最后，我们总结了现有数据集、常用评估指标以及各种方法在公开数据集上的综合评估结果。本文可为刚进入地点识别领域的新手以及对长期机器人定位感兴趣的研究人员提供有价值的教程。我们承诺将在我们的网站 https://github.com/ShiPC-AI/LPR-Survey 上维护一个持续更新的项目。