Autonomous navigation is a key defining feature that allows agricultural robots to perform automated farming tasks. Global navigation satellite system (GNSS) technology is providing autonomous navigation solutions for current commercial robotic platforms that can achieve centimeter-level accuracy when real-time kinematic (RTK) corrections are available. However, GNSS-based solutions are expensive and require a long preparation phase where the field has to be surveyed with a GNSS rover to collect waypoints for the navigation path. An alternative navigation approach can be provided by Local perception sensors, such as LiDAR scanners, by tracking geometric features in the perceived scene. This paper presents a robust LiDAR-based solution for structure tracking along vine rows. The proposed method does not require prior field surveying, and it is insensitive to crop characteristics such as row width and spacing. Moreover, the proposed algorithm identifies and builds an online regression model of the structure. This is done by applying the Hough transform with a parameterization and search method motivated by a practical interpretation of point cloud statistics. The proposed method was tested on a commercial robotic platform in two configurations of vineyards. The experiments show that the proposed algorithm achieves consistent and accurate row tracking, which was validated against a reliable RTK-GNSS ground truth.

自主导航是一个关键的定义特征，它允许农业机器人执行自动化农业任务。全球导航卫星系统 (GNSS) 技术正在为当前的商业机器人平台提供自主导航解决方案，当实时运动学 (RTK) 校正可用时，这些解决方案可以达到厘米级的精度。然而，基于 GNSS 的解决方案成本高昂，并且需要很长的准备阶段，必须使用 GNSS 漫游车对现场进行勘测，以收集导航路径的航点。通过跟踪感知场景中的几何特征，本地感知传感器（例如 LiDAR 扫描仪）可以提供替代导航方法。本文提出了一种强大的基于 LiDAR 的解决方案，用于沿着藤蔓行进行结构跟踪。所提出的方法不需要事先进行实地调查，对行宽、行距等作物特性不敏感。此外，所提出的算法识别并建立了结构的在线回归模型。这是通过应用带有参数化和搜索方法的霍夫变换来完成的，该方法由点云统计的实际解释驱动。所提出的方法在两个葡萄园配置的商业机器人平台上进行了测试。实验表明，所提出的算法实现了一致且准确的行跟踪，并在可靠的 RTK-GNSS 地面实况下进行了验证。这是通过应用带有参数化和搜索方法的霍夫变换来完成的，该方法由点云统计的实际解释驱动。所提出的方法在两个葡萄园配置的商业机器人平台上进行了测试。实验表明，所提出的算法实现了一致且准确的行跟踪，并在可靠的 RTK-GNSS 地面实况下进行了验证。这是通过应用带有参数化和搜索方法的霍夫变换来完成的，该方法由点云统计的实际解释驱动。所提出的方法在两个葡萄园配置的商业机器人平台上进行了测试。实验表明，所提出的算法实现了一致且准确的行跟踪，并在可靠的 RTK-GNSS 地面实况下进行了验证。