3D-vision based detection, localization, and sizing of broccoli heads in the field
3D-vision based detection, localization, and sizing of broccoli heads in the field
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DOI：
10.1002/rob.21726
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发表时间：
2017-12-01
影响因子：
8.3
通讯作者：
Cielniak, Grzegorz
中科院分区：
文献类型：
--
作者：
Kusumam, Keerthy;Krajnik, Tomas;Cielniak, Grzegorz
本文介绍了一种使用低成本RGB-D传感器的机器人收获西兰花的3D视觉系统，该系统是使用在英国和西班牙的真实野外条件下收集的传感数据开发和评估的。所提出的方法解决的任务，检测成熟的西兰花头在外地，并提供其相对于车辆的三维位置。本文评估了不同的3D特征，机器学习和时间滤波方法来检测西兰花头。我们的实验表明，视点特征直方图，支持向量机分类器，和时间过滤器的组合来跟踪检测到的头的结果，在一个系统中，检测西兰花头具有高精度。我们还表明，时间滤波可以用来生成一个3D地图的花椰菜头的位置在该领域。此外，我们提出了自动估计的大小的西兰花头的方法，以确定头是准备收获。所有这些方法都使用来自英国和西班牙的地面实况数据进行了评估，我们还将这些数据提供给研究界，用于后续的算法开发和结果比较。在英国数据集上训练的系统在西班牙数据集上的交叉验证，反之亦然，表明该系统具有良好的泛化能力，证实了低成本3D成像用于商业西兰花收获的强大潜力。
This paper describes a 3D vision system for robotic harvesting of broccoli using low-cost RGB-D sensors, which was developed and evaluated using sensory data collected under real-world field conditions in both the UK and Spain. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning, and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier, and a temporal filter to track the detected heads results in a system that detects broccoli heads with high precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field. Additionally, we present methods for automatically estimating the size of the broccoli heads, to determine when a head is ready for harvest. All of the methods were evaluated using ground-truth data from both the UK and Spain, which we also make available to the research community for subsequent algorithm development and result comparison. Cross-validation of the system trained on the UK dataset on the Spanish dataset, and vice versa, indicated good generalization capabilities of the system, confirming the strong potential of low-cost 3D imaging for commercial broccoli harvesting.