This paper presents a state-of-the-art LiDAR based autonomous navigation system for under-canopy agricultural robots. Under-canopy agricultural navigation has been a challenging problem because GNSS and other positioning sensors are prone to significant errors due to attentuation and multi-path caused by crop leaves and stems. Reactive navigation by detecting crop rows using LiDAR measurements is a better alternative to GPS but suffers from challenges due to occlusion from leaves under the canopy. Our system addresses this challenge by fusing IMU and LiDAR measurements using an Extended Kalman Filter framework on low-cost hardwware. In addition, a local goal generator is introduced to provide locally optimal reference trajectories to the onboard controller. Our system is validated extensively in real-world field environments over a distance of 50.88~km on multiple robots in different field conditions across different locations. We report state-of-the-art distance between intervention results, showing that our system is able to safely navigate without interventions for 386.9~m on average in fields without significant gaps in the crop rows, 56.1~m in production fields and 47.5~m in fields with gaps (space of 1~m without plants in both sides of the row).
翻译:本文展示了以利达AR为基础的用于低冠状农业机器人的先进自主导航系统。低冠状农业导航是一个具有挑战性的问题,因为全球导航卫星系统和其他定位传感器由于作物叶和根叶造成的衰减和多路径而容易发生重大差错。使用利达AR测量法探测作物行是全球定位系统的一个更好的替代办法,但因在树冠下隔绝树叶而面临挑战。我们的系统通过在低成本硬盘上使用扩展卡尔曼过滤框架对IMU和利达AR进行测量来应对这一挑战。此外,还引入了一个本地目标生成器,为机上控制器提供当地最佳参考轨迹。我们的系统在现实世界的实地环境中广泛验证,在不同地点的不同场条件下的多个机器人的距离为50.88米。我们报告的是干预结果之间的距离,显示我们的系统能够安全导航,而没有386.9米的干预,在没有作物行、56.1米至5米的田间和47.5米的田间之间没有明显空隙的田间。