Learned Visual Navigation for Under-Canopy Agricultural Robots
Learned Visual Navigation for Under-Canopy Agricultural Robots
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DOI：
10.15607/rss.2021.xvii.019
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
A. N. Sivakumar;Sahil Modi;M. V. Gasparino;Che Ellis;A. E. B. Velasquez;Girish V. Chowdhary;Saurabh Gupta
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文献类型：
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作者：
A. N. Sivakumar;Sahil Modi;M. V. Gasparino;Che Ellis;A. E. B. Velasquez;Girish V. Chowdhary;Saurabh Gupta
我们描述了一个系统的视觉引导下的树冠农场机器人的自主导航。低成本的树冠下机器人可以在植物树冠下的作物行之间行驶，并完成树冠上无人机或大型农业设备无法完成的任务。然而，在树冠下自主导航它们带来了许多挑战：不可靠的GPS和LiDAR，传感成本高，具有挑战性的农场地形，树叶和杂草造成的混乱，以及季节和作物类型之间外观的巨大变化。我们通过构建一个模块化系统来解决这些挑战，该系统利用机器学习从低成本相机的单目RGB图像中获得鲁棒和可推广的感知，并利用模型预测控制在具有挑战性的地形中进行精确控制。我们的系统CropFollow平均每次干预能够自动驾驶485米，在超过25公里的广泛现场测试中，性能优于最先进的基于激光雷达的系统（每次干预286米）。
We describe a system for visually guided autonomous navigation of under-canopy farm robots. Low-cost under-canopy robots can drive between crop rows under the plant canopy and accomplish tasks that are infeasible for over-the-canopy drones or larger agricultural equipment. However, autonomously navigating them under the canopy presents a number of challenges: unreliable GPS and LiDAR, high cost of sensing, challenging farm terrain, clutter due to leaves and weeds, and large variability in appearance over the season and across crop types. We address these challenges by building a modular system that leverages machine learning for robust and generalizable perception from monocular RGB images from low-cost cameras, and model predictive control for accurate control in challenging terrain. Our system, CropFollow, is able to autonomously drive 485 meters per intervention on average, outperforming a state-of-the-art LiDAR based system (286 meters per intervention) in extensive field testing spanning over 25 km.