| 基于快速点特征直方图的树木点云配准 |
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| 引用本文： | 林榆森,李秋洁,陈婷.基于快速点特征直方图的树木点云配准[J].中国农机化学报,2022(2). |
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| 作者姓名： | 林榆森 李秋洁 陈婷 |
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| 作者单位： | 南京林业大学机械电子工程学院;南京理工大学自动化学院 |
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| 基金项目： | 国家自然科学基金项目(31901239);江苏省农业科技自主创新资金(CX(18)1007)。 |
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| 摘 要： | 随着农林业智能化与信息化发展,树木三维重建技术成为国内外研究的热点。为获取具有颜色信息的树木三维点云,需配准不同视角下的彩色点云数据,为果树三维重建提供基础数据。为此采用仿真树模拟果树,提出一种基于快速点特征直方图(FPFH)的树木点云配准方法,通过添加标定物增加具有稳定FPFH特征的点云个数,从而提高点对配准精度。首先,采用第二代Kinect相机获取树木多个视角的RGB图像和深度图像,通过数据融合、背景去除、滤波等预处理步骤,得到待配准的树木彩色点云数据。然后,使用采样一致性初始配准算法(SAC-IA)根据FPFH特征寻找不同视角下的匹配点对,求取近似变换矩阵。最后,采用最近点迭代算法(ICP)优化初始配准结果。此外,探讨Kinect采集距离、高度、视角差对配准精度的影响,确定最佳的数据采集方式:相机到树木中心的距离为2 m,相机高度为10 cm,每次扫描间隔45°。试验结果表明:设置标定物可以获取鉴别力更强的FPFH特征,提高配准精度,最终配准误差小于1.9 cm。
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| 关 键 词： | 树木点云配准 快速点特征直方图 Kinect相机 采样一致性初始配准 最近点迭代 |
Point cloud registration of tree based on FPFH |
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| Lin Yusen,Li Qiujie,Chen Ting.Point cloud registration of tree based on FPFH[J].Chinese Agricultural Mechanization,2022(2). |
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| Authors: | Lin Yusen Li Qiujie Chen Ting |
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| Institution: | (College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing,210037,China;College of Automation,Nanjing University of Science and Technology,Nanjing,210094,China) |
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| Abstract: | With the development of intelligence and informatization of agriculture and forestry,the 3 D reconstruction technology of trees has gradually become the focus of research at home and abroad.In order to obtain a 3 D morphological model of tree with color information,using simulation to simulate fruit tree,a 3 D point cloud registration scheme of tree in two perspectives based on FPFH(Fast Point Feature Histogram)was proposed.Firstly,the calibration object was set above the target tree.The RGB image and depth image of the trees were obtained simultaneously from different angles using the Kinect camera.With the help of Microsoft’s official development tool Kinect for Windows SDK,the two images were aligned and fused.Then the initial point cloud with color information was generated by PCL(Point cloud library).Secondly,the point cloud of tree and calibration objects was extracted from XYZRGB initial point cloud.Considering the accuracy of acquisition equipment and measurement errors,there were some points far away from the main body in the target point cloud,which would to be eliminated.In order to improve the processing speed of the following point clouds,voxelated mesh sampling was applied to the point cloud.Thirdly,the FPFH of each point on the tree and the calibrator was calculated.According to FPFH,the corresponding points were searched,and initial registration was carried out by SAC-IA(Sample Consensus-initial Alignment).Thus,an approximate transformation matrix was estimated so that two pieces of point cloud got a better initial position.Finally,ICP(Iterative Closest Point)algorithm was used for further registration.In addition,the influence of Kinect acquisition distance,height,and angle difference on registration accuracy was discussed,and the best data acquisition method was determined:the distance from the camera to the center of the tree was 2 m,the height of the camera was 10 cm,and the interval of each scan was 45°.The experiment showed that setting the calibrator can acquire more discriminant FPFH features,which increase the accuracy of corresponding point query in initial registration and then improve the quality of registration,and the final registration error was less than 1.9 cm. |
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| Keywords: | registration of tree point cloud fast point feature histogram Kinect camera sample consensus-initial alignment iterative closest point |
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