果园行间3D LiDAR导航方法

为克服二维激光扫描仪在果园导航中感知信息少、无法有效应对树冠茂密、树干被遮挡等复杂三维果园场景,该研究提出一种基于3D LiDAR的果园行间导航方法。以3D LiDAR为检测设备实时采集果园信息,使用挖空打断后的树墙体心等效树干位置,根据左右树行的最佳平行度对随机采样一致性算法与最小二乘法拟合的树行进行互补融合并求其中心线得到导航线;对纯跟踪算法进行改进,实现差速运动机器人对树行的跟踪。结果表明:系统在篱壁式仿真果园环境下以0.33 m/s的速度沿中心线行走时,绝对航向定位偏差在1.65°以内,绝对横向定位偏差在6.1 cm以内;以0.43 m/s的速度跟踪树行的绝对横向偏差在15 cm以内。在真实梨园下,系统分别以0.68与1.35 m/s的速度跟踪树行,绝对横向偏差分别不超过21.3与22.1 cm。本系统可广泛用于标准果园与复杂三维果园机械的自主导航,具有可靠的稳定性。

Navigation method between rows for orchard based on 3D LiDAR

The fruit industry would suffer a great shock, due mainly to the fact that its yield relies heavily on high labor inputs, but the rural population is aging with the ever-increasing development of cities in China. Autonomous production can bring an effective solution to such issues, further promoting the precision management in orchards. 3D light detection and ranging (LiDAR) sensor has made a much greater contribution to the autonomous navigation in the information acquisition for orchards, compared with the traditional 2D laser scanner. Specifically, LiDAR is a commonly-used remote sensing technique, where a laser is used to measure the distance to an illuminated target. In this study, an inter-row robot navigation was thus proposed in an orchard using 3D LiDAR. The complex three-dimensional scene was treated effectively, particularly with the dense canopy and trunks occluded by branches. A 3D LiDAR detection device was used to collect the environment information at first, and a pass-through filter was then used to correct the region of interest, where the noise was removed from the positioning task. Euclidean clustering was used to recognize the fruit trees around the robot, assuming that the tree branches were subjected to the normal distribution in the vertical direction. Body centers of trees were equivalent to the position of trees. Random sampling consensus and the least square were selected to fit the tree data using the parallelism between the tree rows. A complementary fusion was also put forward to combine two fittings. The centerline between tree rows was calculated and then treated as the target navigation line. In addition, a pure pursuit algorithm was refined using the differential chassis, considering the looking-forward distance and heading deviation. The validation experiments were carried out in a simulated hedgerow orchard and a real pear orchard. It was found that the tree rows successfully fitted with great ability to resist the interference from the environment in the first scenery. The heading positioning deviation was within 1.65°, and the lateral deviation was within 6.1 cm, when the robot walked along the centerline at a speed of 0.33 m/s. The tracking system automatically followed the centerline with a speed of 0.43 m/s, with an absolute lateral deviation of within 15cm. In the second scenery, the tracking system followed the centerline with two speeds of 0.68 and 1.35 m/s, where the absolute lateral deviations were not beyond 21.3 cm and 22.1 cm, respectively. The tracking system can be expected to serve as the automatic navigation with good robustness in standard orchards, including the hedgerow or complex three-dimensional orchard.