Table_1_Research on orchard navigation method based on fusion of 3D SLAM and point cloud positioning.xls

Accurate navigation is crucial in the construction of intelligent orchards, and the need for vehicle navigation accuracy becomes even more important as production is refined. However, traditional navigation methods based on global navigation satellite system (GNSS) and 2D light detection and ranging (LiDAR) can be unreliable in complex scenarios with little sensory information due to tree canopy occlusion. To solve these issues, this paper proposes a 3D LiDAR-based navigation method for trellis orchards. With the use of 3D LiDAR with a 3D simultaneous localization and mapping (SLAM) algorithm, orchard point cloud information is collected and filtered using the Point Cloud Library (PCL) to extract trellis point clouds as matching targets. In terms of positioning, the real-time position is determined through a reliable method of fusing multiple sensors for positioning, which involves transforming the real-time kinematics (RTK) information into the initial position and doing a normal distribution transformation between the current frame point cloud and the scaffold reference point cloud to match the point cloud position. For path planning, the required vector map is manually planned in the orchard point cloud to specify the path of the roadway, and finally, navigation is achieved through pure path tracking. Field tests have shown that the accuracy of the normal distributions transform (NDT) SLAM method can reach 5 cm in each rank with a coefficient of variation that is less than 2%. Additionally, the navigation system has a high positioning heading accuracy with a deviation within 1° and a standard deviation of less than 0.6° when moving along the path point cloud at a speed of 1.0 m/s in a Y-trellis pear orchard. The lateral positioning deviation was also controlled within 5 cm with a standard deviation of less than 2 cm. This navigation system has a high level of accuracy and can be customized to specific tasks, making it widely applicable in trellis orchards with autonomous navigation pesticide sprayers.

精确导航在智能果园的建设中至关重要，而随着生产精度的提升，对车辆导航准确性的需求愈发显著。然而，基于全球导航卫星系统（GNSS）和二维激光雷达（LiDAR）的传统导航方法，在树木冠层遮挡导致的感测信息匮乏的复杂场景中，其可靠性可能不足。为解决这些问题，本文提出了一种适用于棚架果园的基于3D LiDAR的导航方法。通过采用3D LiDAR与3D同步定位与建图（SLAM）算法，利用点云库（PCL）收集并过滤果园点云信息，提取棚架点云作为匹配目标。在定位方面，通过融合多个传感器的可靠定位方法确定实时位置，包括将实时动力学（RTK）信息转换为初始位置，并对当前帧点云与棚架参考点云进行正态分布变换以匹配点云位置。至于路径规划，在果园点云中手动规划所需的矢量地图，以指定道路路径，最终通过纯路径跟踪实现导航。实地测试表明，正常分布变换（NDT）SLAM方法的精度可达每等级5厘米，变异系数低于2%。此外，当以1.0米/秒的速度沿路径点云移动时，该导航系统的定位航向精度高，偏差在1°以内，标准差小于0.6°。横向定位偏差也被控制在5厘米以内，标准差小于2厘米。此导航系统具有较高的精度，并可定制以满足特定任务，因此广泛应用于配备自主导航农药喷雾机的棚架果园。