面向采摘机器人的棉花激光定位算法
为了定位棉株上的棉花，设计了1个激光测距试验装置，利用计算机图像处理技术和模式识别理论结合棉花的农学特性测量了单朵棉花的三维坐标。以0.004m为X、Y轴的采样间距，获取传感器至棉株表面点云的距离图像；以传感器至棉花上表面的最长距离0.9m为阈值对棉株距离图像进行二值化处理，去除地面背景；以棉枝的宽度0.01～0.02m为结构元素尺寸，对二值图进行形态学开运算，去除棉枝，提取棉花区域。用欧氏距离计算像素之间的相似度，用Cophenetic相关系数选择质心距离为类间距离，以呈45°夹角棉枝的最小纵向间距0.17m为阈值，对棉花距离图像进行层次聚类，分割粘连重叠的棉花，求取单朵棉花的三维坐标。结果...
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| Published in | 农业工程学报 Vol. 30; no. 14; pp. 42 - 48 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
南京农业大学工学院，江苏省现代设施农业技术与装备工程实验室，南京 210031
2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.3969/j.issn.1002-6819.2014.14.006 |
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| Abstract | 为了定位棉株上的棉花，设计了1个激光测距试验装置，利用计算机图像处理技术和模式识别理论结合棉花的农学特性测量了单朵棉花的三维坐标。以0.004m为X、Y轴的采样间距，获取传感器至棉株表面点云的距离图像；以传感器至棉花上表面的最长距离0.9m为阈值对棉株距离图像进行二值化处理，去除地面背景；以棉枝的宽度0.01～0.02m为结构元素尺寸，对二值图进行形态学开运算，去除棉枝，提取棉花区域。用欧氏距离计算像素之间的相似度，用Cophenetic相关系数选择质心距离为类间距离，以呈45°夹角棉枝的最小纵向间距0.17m为阈值，对棉花距离图像进行层次聚类，分割粘连重叠的棉花，求取单朵棉花的三维坐标。结果表明，单朵棉花的识别率达96.67%，激光测距与手工测量结果之间的相关系数为0.9934。该研究为采摘机器人运动轨迹的规划提供了依据。 |
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| AbstractList | 为了定位棉株上的棉花，设计了1个激光测距试验装置，利用计算机图像处理技术和模式识别理论结合棉花的农学特性测量了单朵棉花的三维坐标。以0.004m为X、Y轴的采样间距，获取传感器至棉株表面点云的距离图像；以传感器至棉花上表面的最长距离0.9m为阈值对棉株距离图像进行二值化处理，去除地面背景；以棉枝的宽度0.01～0.02m为结构元素尺寸，对二值图进行形态学开运算，去除棉枝，提取棉花区域。用欧氏距离计算像素之间的相似度，用Cophenetic相关系数选择质心距离为类间距离，以呈45°夹角棉枝的最小纵向间距0.17m为阈值，对棉花距离图像进行层次聚类，分割粘连重叠的棉花，求取单朵棉花的三维坐标。结果表明，单朵棉花的识别率达96.67%，激光测距与手工测量结果之间的相关系数为0.9934。该研究为采摘机器人运动轨迹的规划提供了依据。 TP242%S126; 为了定位棉株上的棉花，设计了1个激光测距试验装置，利用计算机图像处理技术和模式识别理论结合棉花的农学特性测量了单朵棉花的三维坐标。以0.004m为X、Y轴的采样间距，获取传感器至棉株表面点云的距离图像；以传感器至棉花上表面的最长距离0.9m为阈值对棉株距离图像进行二值化处理，去除地面背景；以棉枝的宽度0.01～0.02m为结构元素尺寸，对二值图进行形态学开运算，去除棉枝，提取棉花区域。用欧氏距离计算像素之间的相似度，用Cophenetic相关系数选择质心距离为类间距离，以呈45°夹角棉枝的最小纵向间距0.17m为阈值，对棉花距离图像进行层次聚类，分割粘连重叠的棉花，求取单朵棉花的三维坐标。结果表明，单朵棉花的识别率达96.67%，激光测距与手工测量结果之间的相关系数为0.9934。该研究为采摘机器人运动轨迹的规划提供了依据。 |
| Abstract_FL | In order to detect cotton’s position on a plant, a laser measurement experiment was designed, which included a crossgirder, and beneath which a model LMS291-S05 laser scanner produced by SICK Co., Ltd was fixed. Then the three-dimensional coordinate values of a single cotton plant can be measured with the devices mentioned above, and the acquired distance image was furthermore processed with computer image processing technology and pattern recognition theory and also with the agronomic characteristics of cotton taken into consideration. The sampling interval of the laser sensor on axis X and axis Y was both 0.004 m, and the sampled points formed a image that illustrated the distance from the laser sensor to the plant surface point cloud. For the purpose of removing the unneeded background of the plant in the image, the acquired plant distance image was first processed with binaryzation at the threshold value of 0.9 m, because that 0.9 m happened to be the longest distance from the laser sensor to the bottom part of the cotton plant surface according to the measured data. Observing the cotton plant’s morphological characters in the formerly binarized image, and it was not hard to find out that the cotton part in the image appeared to be circular with a large area, while the stalk part in the image was thin and tiny. On this basis, morphology opening operations were carried out toward the edge of the bimarized image with the structural elements from a circle whose radius ranges from 0.01 m to 0.02 m, which is the usual width of a cotton stalk, in order to remove the unneeded cotton stalk background and extract the binary image of cotton. After a series of comparisons among different distances namely Euclidean, Mahalanobis, Bullock, and Minkowski distances, and considering that the pixel gray value was one-dimensional data, the distance between pixels was found to be insensitive to computing methods. The similarity between pixels in the image was calculated with Euclidean distance, and the centroid distance was chosen, which was also decided among several other different distances like closest distance, longest distance, and sums of squares of deviations, as the between-class distance according to the Cophenetic correlation coefficient. And due to the fact that branches that were 45° to each other usually at least had two more branches in between them vertically, and the minimum vertical distance of two neighboring branches was about 0.05 m, correspondingly the minimum vertical distance of two pieces of overlapped cotton was about 0.17 m, so the cotton image clustered at the threshold of 0.17 m. Then the adhesion or overlapped cotton was segmented, and the three-dimensional coordinate values of a single cotton plant were acquired. The result of a series of experiments showed that the recognition rate of a single cotton plant was as high as 96.67%, the mean measurement error of the cotton was 0.015 m, and the relative error was 2.43%. The correlation coefficient between the laser measurement and manual measurement results was up to 0.9934, which was high enough to provide the picking robot with needed parameters that could be used to determine its movement locus. |
| Author | 王玲 刘思瑶 卢伟 顾宝兴 朱镕杰 朱宏超 |
| AuthorAffiliation | 南京农业大学工学院江苏省现代设施农业技术与装备工程实验室,南京210031 |
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| Author_FL | Wang Ling Gu Baoxing Liu Siyao Lu Wei Zhu Hongchao Zhu Rongjie |
| Author_FL_xml | – sequence: 1 fullname: Wang Ling – sequence: 2 fullname: Liu Siyao – sequence: 3 fullname: Lu Wei – sequence: 4 fullname: Gu Baoxing – sequence: 5 fullname: Zhu Rongjie – sequence: 6 fullname: Zhu Hongchao |
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| DocumentTitleAlternate | Laser detection method for cotton orientation in robotic cotton picking |
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| Keywords | 激光测距 image processing 机器人 层次聚类 形态学运算 棉花 图像处理 laser measurement cotton robots morphological operation hierarchical cluster |
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| Notes | 11-2047/S image processing;robots;cotton;laser measurement;morphological operation;hierarchical cluster In order to detect cotton’s position on a plant, a laser measurement experiment was designed, which included a crossgirder, and beneath which a model LMS291-S05 laser scanner produced by SICK Co., Ltd was fixed. Then the three-dimensional coordinate values of a single cotton plant can be measured with the devices mentioned above, and the acquired distance image was furthermore processed with computer image processing technology and pattern recognition theory and also with the agronomic characteristics of cotton taken into consideration. The sampling interval of the laser sensor on axis X and axis Y was both 0.004 m, and the sampled points formed a image that illustrated the distance from the laser sensor to the plant surface point cloud. For the purpose of removing the unneeded background of the plant in the image, the acquired plant distance image was first processed with binaryzation at the threshold value |
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| Publisher | 南京农业大学工学院，江苏省现代设施农业技术与装备工程实验室，南京 210031 |
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| Snippet | 为了定位棉株上的棉花，设计了1个激光测距试验装置，利用计算机图像处理技术和模式识别理论结合棉花的农学特性测量了单朵棉花的三维坐标。以0.004m为X、Y轴的采样间距，获取... TP242%S126; 为了定位棉株上的棉花，设计了1个激光测距试验装置，利用计算机图像处理技术和模式识别理论结合棉花的农学特性测量了单朵棉花的三维坐标。以0.004m为X、Y轴的采... |
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| SubjectTerms | 图像处理 层次聚类 形态学运算 机器人 棉花 激光测距 |
| Title | 面向采摘机器人的棉花激光定位算法 |
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