摘要
提出了基于点云边界特征点的改进迭代最近点(ICP)方法来提高逆向工程中点云数据配准的效率和精度。首先,提出了基于点云边界特征点的初始配准方法。对点云最小包围盒进行三维空间划分,建立空间网格模型;运用边界种子网格识别及生长算法,从点云边界提取特征点,运用奇异值矩阵分解法(SVD)求出点云的变换矩阵,得到初始配准结果。然后,提出了改进的ICP精确配准方法。对点云对应点赋予权重,剔除权重大于阈值的点,通过对目标函数引入M-估计(M-estimation),剔除异常点。最后,在初始配准的基础上,运用改进的ICP方法精确配准。对经典ICP方法和改进ICP方法做对比实验,结果显示,改进方法的配准效率提高了70%以上,误差减小到0.02%。实验表明,本文方法大幅提高了点云配准的效率和精度。
An improved Iterative Closest Point （ICP） method based on the boundary feature points of the point cloud is proposed to improve the efficiency and accuracy of point cloud data registration in re- verse engineering fields. First, an initial registration method based on the boundary feature points of point cloud is proposed. The method partitions the minimum bounding box of point cloud with grids in a 3D space, and sets up the space grid model. Then, it applies boundary seed grid recognition and growth algorithms to extract feature points from the boundary of point cloud, and works out the transformation matrix using Singular Value Decomposition （SVD） method to get the results of initial registration. Furthermore, an improved ICP accurate registration method is presented. It weighs the corresponding points of the point cloud, eliminates the points whose weight is larger than the thresh- old, and introduces M-estimation to the objective function to eliminate the abnormal points. Finally, the point cloud is accurately registered by the improved ICP method on the basis of initial registration. Compared with original ICP method, the improved ICP method increases the efficiency by more than 70 per- cent and reduces the error to 0.02 percent. The experiment results indicate that the method proposed in this paper improves the efficiency and accuracy of point cloud registration greatly.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2012年第9期2068-2077,共10页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.60905022)
吉林省科技发展计划资助项目(No.201105016)
吉林大学符号计算与知识工程教育部重点实验室开放基金资助项目
关键词
逆向工程
迭代最近点
点云配准
边界特征点
M-估计
reverse engineering
Iterative Closest Point（ICP）
point cloud registration
feature pointsof boundary
M-estimation
