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, weeds 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：自主导航对于农业中的各类机器人应用至关重要。然而，许多现有方法依赖RTK-GPS系统，该系统成本高昂且易受信号覆盖不佳的影响。本文介绍了一种先进的基于LiDAR的导航系统，该能在作物行田地中实现冠层上方自主导航，即使冠层完全遮蔽行间间距。我们提出的作物行检测算法可在多种场景下检测作物行，涵盖不同作物类型、生长阶段、杂草存在以及作物行内的不连续性。在不利用机器人全局定位的情况下，我们的导航系统能够在这些具有挑战性的场景中执行自主导航，检测作物行的末端，并自主导航至下一个作物行，提供一种与作物无关的方法来导航整个作物行田地。该导航系统已在多种模拟农业田地中经过测试，在定制的Amiga机器人上实现了平均2.98厘米的自主行驶精度，且无需人工干预。此外，来自实际大豆田地的作物行检测算法定性结果验证了我们基于LiDAR的作物行检测算法在实际农业应用中的潜力。