基于邻域几何特征约束的植株三维形态配准方法研究

摘要

为提高不同角度多次测量得到的植株点云配准速度和精度，提出一种基于植株点云邻域几何特征约束改进的三维形态配准方法。首先，针对点云量大并缺少拓扑信息，选取关键点集并估计其中每个点的支撑邻域来拟合出支撑曲面，进一步计算出邻域几何特征。其次，采用特征相似度的方法实现点云的初始配准。最后，在初始配准的基础上，加入两个新的夹角几何特征约束匹配点对改进 ICP 算法进行配准优化。利用 bunny、兵马俑模型点云对算法的精度和通用性进行测试，并在实际应用中验证了配准效果和算法鲁棒性。结果表明，与传统的特征配准方法相比，该方法配准速度提高约10％以上，精确配准误差约为传统算法误差的1％。%In order to improve the registration speed and precision of plant point cloud derived from multiple measurements in various angles,this paper proposes an improved 3D morphological registration method which is based on the geometrical feature constraint of neighbourhood of plant point clouds.First,aiming at the large amount of point clouds and lack of topological information,the method estimates the support neighbourhood of each point by selecting key point set to fit the support surface and then to further compute the geometrical feature of neighbourhood.Secondly,it employs the feature similarity method to implement the initial registration of point clouds. Finally,based on initial registration it adds two matching pairs of angle’s geometric feature constraints to improve the iterative closest point (ICP)algorithm and to optimise the registration.We tested the accuracy and universality of the algorithm with the point clouds of bunny and Terracotta Army models,and verified in practical application the effect of registration and the robustness of the algorithm.Results showed that compared with traditional feature-based registration method,the proposed method increased the registration speed about over 11.9%,and the error of precise registration was about 1% of that of the traditional feature-based algorithm.