针对传统匹配方法存在匹配精度低、速度慢等问题,提出一种基于扩展点特征直方图(EPFH)特征的点云匹配算法,该算法采用先粗配再细配的策略。利用ISS (intrinsic shape signature)特征检测算法获取点云上的显著特征点集;对特征点进行EPFH特征描述;通过采样一致性算法估算刚体变换矩阵,完成待匹配点云和目标点云的初始匹配;接着使用基于k-d树的迭代最近点算法实现两片点云的精细匹配;最后将本文算法分别应用于公共数据集和兵马俑特殊数据集进行实验验证。实验结果表明,相比传统方法而言,本文算法具有更高的匹配精度和速度。

This paper proposes a point cloud registration algorithm based on extended point feature histogram (EPFH) feature. In the proposed algorithm, the strategy of rough registration prior to fine registration is adopted. The purpose of this paper is to overcome the problems of low registration accuracy and slow speed which are encountered in traditional registration methods. Initially, the intrinsic shape signature (ISS) feature-detection algorithm is used to obtain the salient feature-point set on the point clouds. Then, the EPFH feature description is applied on these feature points. Subsequently, the rigid body transformation matrix is estimated using the sampling consistency algorithm to complete the initial registration of the point clouds and the target point clouds. The k-d tree-based iterative nearest-point algorithm is used to implement the fine registration of the two-point clouds. Finally, experimental verification is performed by applying the proposed algorithm to the public data set and the terracotta warrior data set. The experimental results show that the proposed algorithm exhibits higher registration accuracy and higher speed than traditional methods.