Autonomous navigation is crucial for various robotics applications in agriculture. However, many existing methods depend on RTK-GPS systems, which are expensive and susceptible to poor signal coverage. This paper introduces a state-of-the-art LiDAR-based navigation system that can achieve over-canopy autonomous navigation in row-crop fields, even when the canopy fully blocks the interrow spacing. Our crop row detection algorithm can detect crop rows across diverse scenarios, encompassing various crop types, growth stages, weed presence, and discontinuities within the crop rows. Without utilizing the global localization of the robot, our navigation system can perform autonomous navigation in these challenging scenarios, detect the end of the crop rows, and navigate to the next crop row autonomously, providing a crop-agnostic approach to navigate the whole row-crop field. This navigation system has undergone tests in various simulated agricultural fields, achieving an average of $2.98cm$ autonomous driving accuracy without human intervention on the custom Amiga robot. In addition, the qualitative results of our crop row detection algorithm from the actual soybean fields validate our LiDAR-based crop row detection algorithm's potential for practical agricultural applications.

翻译：自主导航对于农业领域的各类机器人应用至关重要。然而，现有方法多依赖RTK-GPS系统，其价格昂贵且易受信号覆盖不良的影响。本文提出一种先进的基于激光雷达的导航系统，能够在行播作物田实现冠层上方自主导航，即使冠层完全遮蔽行间空隙。我们的作物行检测算法可检测多种场景下的作物行，涵盖不同作物类型、生长阶段、杂草存在以及作物行中断等情况。在不使用机器人全局定位的情况下，本导航系统能在这些具有挑战性的场景中执行自主导航，检测作物行末端，并自主导航至下一行作物，提供一种适用于整个行播作物田的作物无关导航方法。该导航系统已在多种模拟农业场地进行测试，在定制化Amiga机器人上实现了平均2.98cm的自主行驶精度（无需人工干预）。此外，在实际大豆田中获取的作物行检测算法定性结果，验证了基于激光雷达的作物行检测算法在实际农业应用中的潜力。