基于模型的3D点云分割，用于自动选择西兰花收获*

UK-RAS19 Conference: "Embedded Intelligence: Enabling and Supporting RAS Technologies" Proceedings Pub Date : 2019-01-24 DOI:10.31256/UKRAS19.7

Hector A. Montes, Grzegorz Cielniak, T. Duckett

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摘要

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

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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UK-RAS19 Conference: "Embedded Intelligence: Enabling and Supporting RAS Technologies" Proceedings

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