LiDAR has gained popularity in autonomous driving due to advantages like long measurement distance, rich 3D information, and stability in harsh environments. Place Recognition (PR) enables vehicles to identify previously visited locations despite variations in appearance, weather, and viewpoints, even determining their global location within prior maps. This capability is crucial for accurate localization in autonomous driving. Consequently, LiDAR-based Place Recognition (LPR) has emerged as a research hotspot in robotics. However, existing reviews predominantly concentrate on Visual Place Recognition (VPR), leaving a gap in systematic reviews on LPR. This paper bridges this gap by providing a comprehensive review of LPR methods, thus facilitating and encouraging further research. We commence by exploring the relationship between PR and autonomous driving components. Then, we delve into the problem formulation of LPR, challenges, and 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 and evaluation metrics and envision promising future directions. This paper can serve as a valuable tutorial for newcomers entering the field of place recognition. We plan to maintain an up-to-date project on https://github.com/ShiPC-AI/LPR-Survey.

中文翻译：

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基于 LiDAR 的自动驾驶地点识别：一项调查


LiDAR 因其测量距离长、3D 信息丰富、在恶劣环境下稳定性等优点，在自动驾驶中受到广泛应用。地点识别 （PR） 使车辆能够识别以前访问过的位置，尽管外观、天气和视点各不相同，甚至可以在之前的地图中确定它们的全局位置。此功能对于自动驾驶中的准确定位至关重要。因此，基于 LiDAR 的位置识别 （LPR） 已成为机器人领域的研究热点。然而，现有的综述主要集中在视觉场所识别 （VPR） 上，在 LPR 的系统综述中留下了空白。本文通过对 LPR 方法进行全面回顾来弥合这一差距，从而促进和鼓励进一步的研究。我们首先探讨了 PR 和自动驾驶组件之间的关系。然后，我们深入研究了 LPR 的问题表述、挑战以及与先前调查的关系。随后，我们对相关研究进行了深入的回顾，其中提供了详细的分类、优缺点和架构。最后，我们总结了现有的数据集和评估指标，并设想了有希望的未来方向。本文可以作为进入地方识别领域的新人有价值的教程。我们计划在 https://github.com/ShiPC-AI/LPR-Survey 上保持最新的项目。