【摘要】 针对传统点云配准算法精度低、易产生误匹配以及后续系列改进算法中点特征较为单一对点云形状存在描述误差等问题，提出一种基于点云组合特征点和主成分分析的点云配准算法。对点云提取内部形态描述子，采用AC算法提取点云轮廓点（BDRY）组成组合特征点（ISS＿BDRY）；计算ISS＿BDRY特征点的法线并利用快速点特征直方图进行描述，之后采用结合主成分分析改进的采样一致性初始配准算法SAC-IA来最小化点云主轴间的距离误差，进而降低点云精配准过程中的迭代次数，为后续点云配准提供良好的位姿。精配准阶段引入KD-Tree加速搜索点云的迭代最近点配准算法进行配准。实验结果表明，提取组合特征点相比于其他单点特征在Cat和Michael点云上配准精度达到10-8数量级，粗配准阶段采用组合特征法使配准精度分别提升65.19%和44.77%，精配准阶段相比于ICP、NDT、Super 4PCS等算法精度达到10-16数量级，几乎完全重合。更多还原

【Abstract】 Aiming at the problems of low accuracy,easy mismatching,and the descriptive error of single point features in the subsequent series of improved algorithms to point cloud shape,a point cloud registration algorithm based on point cloud combination feature point and principal component analysis is proposed. The intrinsic shape signatures is extracted from the point cloud,and the AC algorithm is used to extract the boundary points(BDRY)of the point cloud to form the combined feature points(ISS＿BDRY). The normal of the ISS＿BDRY feature point is calculated and described by fast point feature histogram,and then the sampling consistency initial registration algorithm improved by principal component analysis SAC-IA is used to minimize the distance error between the main axes of the point cloud,thereby reducing the number of iterations in the point cloud fine registration process,and providing good pose for subsequent point cloud registration. In the fine registration stage,the iterative closest point registration algorithm introduced KD-Tree to accelerate search point cloud is used for registration. The experimental results show that compared with other single-point features,the registration accuracy of extracted combined feature points on Cat and Michael point clouds reaches 10-8orders of magnitude,and the registration accuracy of the combined feature method is increased by 65.19% and 44.77%,respectively. Compared with ICP,NDT,Super 4PCS and other algorithms,the accuracy of the fine registration stage reaches 10-16orders of magnitude,and it is almost completely coincide.更多还原