基于颜色取样的苹果树枝干点云数据提取方法
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国家重点研发计划项目(2017YFD0700503)和河北省高等学校科学技术研究项目(QN2017417)


Point Cloud Extraction of Apple Tree Canopy Branch Based on Color Sampling
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    摘要:

    为了快速提取苹果树冠层枝干三维点云数据,以不同生长时期苹果树冠层彩色点云数据为研究对象,提出了基于颜色取样的苹果树枝干点云数据提取方法。首先,提出了苹果树冠层彩色点云获取方法,利用Trimble TX8型地面三维激光扫描仪获取冠层点云数据,同轴全景摄像机获取彩色全景图,采用贴图方法着色点云数据;然后,提取全景图像苹果树冠层区域R、G、B颜色分量信息,根据其分布规律建立枝干部分自适应分割阈值,并根据颜色阈值删除冠层中非枝干部分彩色点云数据;最后,在Geomagic软件中经过封装—创建流形—编辑多边形—填充孔—光滑等一系列操作重建枝干三维模型。苹果树提取枝干点云数据实验结果表明,本文方法点云删除率为75.74%,相对于人工枝干点云数据提取,侧枝数量平均准确率为93.34%,效率提高200倍以上,大大缩短了冠层枝干三维重建时间。本研究成果可为有叶苹果树枝干动力学模型建立提供技术基础。

    Abstract:

    Construction of 3D model of tree is a longterm research hotspot in botany, computer graphics, and architecture. And tree canopy branch reconstruction is an important component in the canopy dynamics analysis. The emergence of terrestrial laser scanners has accelerated this reconstruction process. To quickly reconstruct the canopy branch model, it is necessary to delete a large number of nonbranched interference point clouds. Taking the canopy of apple tree in the maturity growth stage as the research object, a method of colorbased sampling apple tree canopy trunk point cloud extraction was proposed. Firstly, the apple tree canopy color point cloud acquisition method was proposed. Trimble TX8 and coaxial panoramic camera were selected as the data acquisition device to acquire the apple canopy color point cloud data. Point clouds and color panoramic photos were matched in Realworks software, and color point clouds were get. Then, the color information R, G and B in the panoramic image was extracted. The adaptive segmentation threshold was established according to the distribution rules of R, G and B in the panoramic image branch area. Color point cloud data of the nonbranch part in the canopy was deleted according to the threshold. Finally, the 3D branch model was reconstructed in the Geomgic software. The process was followed by a series of operations, such as wrap, manifold creation, polygon editing, hole filling and smoothing. The experimental results of the apple tree branch extraction point cloud data showed that the point cloud deletion rate of this method was 75.74%. Compared with the artificial branch point cloud data extraction, the side branch accuracy rate was 93.34%, and the efficiency was improved by more than 200 times, shortening the threedimensional reconstruction time of canopy branches. In this way, the results of this study can provide a basis for studying the canopy structure analysis and the establishment of the branching dynamics model of the leafy apple tree.

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郭彩玲,刘刚.基于颜色取样的苹果树枝干点云数据提取方法[J].农业机械学报,2019,50(10):189-196.

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  • 收稿日期:2019-05-22
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  • 在线发布日期: 2019-10-10
  • 出版日期: 2019-10-10