An optimal solution to the localization problem is essential for developing autonomous robotic systems. Apart from autonomous vehicles, precision agriculture is one of the elds that can bene t most from these systems. Although LiDAR place recognition is a widely used technique in recent years to achieve accurate localization, it is mostly used in urban settings. However, the lack of distinctive features and the unstructured nature of agricultural environments make place recognition challenging. This work presents a comprehensive review of state-of-the-art the latest deep learning applications for agricultural environments and LPR techniques. We focus on the challenges that arise in these environments. We analyze the existing approaches, datasets, and metrics used to evaluate LPR system performance and discuss the limitations and future directions of research in this eld. This is the rst survey that focuses on LiDAR based localization in agricultural settings, with the aim of providing a thorough understanding and fostering further research in this specialized domain.

翻译：定位问题的最优解决方案对于开发自主机器人系统至关重要。除了自动驾驶车辆，精准农业是能够从这些系统中获益最多的领域之一。尽管LiDAR地点识别是近年来实现精确定位的常用技术，但其主要应用于城市环境。然而，农业环境中缺乏显著特征且结构非规则化的特点，使得地点识别面临挑战。本文对农业环境中的最新深度学习应用及LiDAR地点识别技术进行了全面综述，重点关注这些环境中出现的特定挑战。我们分析了现有方法、用于评估LiDAR地点识别系统性能的数据集与指标，并讨论了该领域研究的局限性与未来方向。这是首篇专注于农业场景中基于LiDAR定位技术的综述，旨在提供深入理解并推动这一专业领域的进一步研究。