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Localization for precision navigation in agricultural fields—Beyond crop row following
Journal of Field Robotics ( IF 8.3 ) Pub Date : 2020-11-09 , DOI: 10.1002/rob.21995
Wera Winterhalter 1 , Freya Fleckenstein 1 , Christian Dornhege 1 , Wolfram Burgard 1
Affiliation  

The growing world population calls for more efficient and sustainable farming technologies. Automating agricultural tasks has great potential to improve farming technologies. A key requirement for full automation is the ability of agricultural vehicles to accurately navigate entire fields without damaging value crops. One important precondition for autonomous navigation is localization, that is, the ability of a vehicle to accurately estimate its pose relative to the crops. A majority of localization approaches detect crop rows to track the heading and lateral offset of the vehicle. This is sufficient to guide the vehicle along crop rows while driving inside the field. However, switching between rows requires a longitudinal pose estimate to determine when to turn at the end of the field. Additionally, at the end of the field sensor data contains less crop row structure and more noise from wild growing vegetation. This can lead to false‐positive crop row detections. In this paper, we present a localization approach that goes beyond state‐of‐the‐art crop row following algorithms by providing robust pose estimates not only inside the field but also at the end of the field. The underlying concept of our approach is to estimate the vehicle pose relative to a global navigation satellite system (GNSS)‐referenced map of crop rows. This allows us to fuse crop row detections with GNSS signals to obtain a pose estimate with the accuracy comparable to a row following approach in the heading and lateral offset, while at the same time maintaining at least GNSS accuracy along the row. Employing a GNSS‐referenced map of crop rows poses several challenges. To relate the detected crop rows to those in the map, we propose a data association strategy that finds correspondences between two sets of lines, that is, crop rows. Furthermore, we improve the GNSS‐based longitudinal pose estimate by detecting the end of the field from vegetation data. Additionally, we introduce a novel method to determine false‐positive crop row detections to increase the overall robustness in particular in challenging scenarios at the end of the field. Extensive real‐world experiments on three different types of crops demonstrate that our localization approach is well suited for fully autonomous navigation in entire fields.

中文翻译:

在农业领域中进行精确导航的本地化-超越农作物行

不断增长的世界人口需要更有效和可持续的农业技术。使农业任务自动化具有巨大的潜力,可以改善农业技术。完全自动化的关键要求是农用车辆能够在不损害有价作物的情况下准确导航整个田地的能力。自主导航的一个重要前提是定位,即车辆准确估计其相对于农作物的姿态的能力。大多数定位方法会检测农作物行,以跟踪车辆的航向和横向偏移。这足以在田间行驶时沿着农作物行引导车辆。然而,在行之间切换需要纵向姿势估计以确定何时在场的尽头转弯。此外,在田间传感器的最后,数据包含较少的作物行结构和来自野生植物的更多噪声。这可能会导致作物行检测结果为假阳性。在本文中,我们提出了一种本地化方法,该方法不仅提供了最新的农作物行跟踪算法,还不仅提供了田间内部而且还提供了田间末端的可靠姿态估计。我们方法的基本概念是相对于以全球导航卫星系统(GNSS)为参考的农作物行图估计车辆姿态。这使我们能够将农作物行的检测与GNSS信号融合在一起,从而获得姿态估计,其航向和侧向偏移的精度可与行跟踪方法相提并论,同时至少沿行保持GNSS精度。使用GNSS参照的作物行图会带来一些挑战。为了将检测到的农作物行与地图中的农作物行关联起来,我们提出了一种数据关联策略,该策略可以找到两组线(即农作物行)之间的对应关系。此外,我们通过从植被数据中检测出场的末端,改进了基于GNSS的纵向姿态估计。此外,我们引入了一种新颖的方法来确定假阳性作物行检测,以提高整体鲁棒性,尤其是在田间挑战性的情况下。在三种不同类型的农作物上进行的大量实际实验表明,我们的本地化方法非常适合在整个田地中进行完全自主的导航。我们通过从植被数据中检测出场的末端来改进基于GNSS的纵向姿态估计。此外,我们引入了一种新颖的方法来确定假阳性作物行检测,以提高整体鲁棒性,尤其是在田间挑战性的情况下。在三种不同类型的农作物上进行的大量实际实验表明,我们的本地化方法非常适合在整个田地中进行完全自主的导航。我们通过从植被数据中检测出场的末端来改进基于GNSS的纵向姿态估计。此外,我们引入了一种新颖的方法来确定假阳性作物行检测,以提高整体鲁棒性,尤其是在田间挑战性的情况下。在三种不同类型的农作物上进行的大量实际实验表明,我们的本地化方法非常适合在整个田地中进行完全自主的导航。
更新日期:2020-11-09
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