摘要
针对多视点云的配准问题,提出了基于球面特征的自动配准方法。在测量的零件周围固定标准球,把零件和标准球作为一个整体进行点云测量。用球面拟合的方法求解标准球的球心坐标,并在待配准点云的球心坐标中搜索对应点,从而计算粗配准中的旋转矩阵和平移矩阵,实现点云的粗配准,采用融入球心坐标信息的改进的ICP算法(迭代最近点法)实现点云的精配准。这种方法大大缩少了粗配准中对应点的搜索范围,并实现了自动配准,提高了配准效率,改进的ICP算法增强了配准算法的鲁棒性,实例证明该方法有效。
For multi-view point cloud of registration,an automatic matching algorithm based on the spherical feature was proposed. The standard balls around the measurement parts was fixed,and then the parts and standard balls as a whole was been measured. The spherical center coordinates was found via spherical fitting. Then the corresponding points in the spherical center coordinates belong to the point cloud to be registered was searched,and the corresponding points to calculate the registration of rotation matrix and translation matrix was used,those was used to point cloud of coarse registration. Finally,an improved ICP( Iterative Closest Point) algorithm was used to integrate the sphere coordinate information center to the point cloud of precise registration. The present registration method greatly reduced the coarse registration of corresponding point search,and the automatic registration was been implemented. The present algorithm also improved the registration efficiency and the improved ICP algorithm enhances the robustness of matching algorithm. The examples verifies that the method is effective.
出处
《机械科学与技术》
CSCD
北大核心
2015年第12期1851-1856,共6页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(51075362)
浙江省自然科学基金项目(Y1100073)
宁波市产业技术创新及成果产业化重点项目(2013B10022)资助
关键词
自动配准算法
球面特征
点云配准
旋转矩阵
平移矩阵
algorithms
automobiles
calculations
computational efficiency
efficiency
feature extraction
flowcharting
matrix algebra
measurements
robustness(control systems)
automatic matching algorithm
point cloud registration
rotation matrix
spherical feature
translation matrix
