Model based 3D point cloud segmentation for automated selective broccoli harvesting*

Hector A. Montes, Grzegorz Cielniak, T. Duckett
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Abstract

The 3D point cloud data was captured in outdoor fields under different weather conditions in 4 locations: 2 in the UK, 1 in Spain, and 1 more in USA using the Kinect 2 sensor. Histograms of the reference models used in our algorithm. A FPFH descriptor [1], based on a set of angular features, is computed for each data point. The descriptor is then matched to both reference models and the difference provides the final classification score. Reference models
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基于模型的3D点云分割,用于自动选择西兰花收获*
3D点云数据是在不同天气条件下在4个地点的户外场地捕获的:2个在英国,1个在西班牙,1个在美国,使用Kinect 2传感器。我们算法中使用的参考模型的直方图。基于一组角度特征,为每个数据点计算FPFH描述符[1]。然后将描述符与两个参考模型进行匹配,差异提供最终的分类分数。参考模型
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