针对当前机器视觉热点研究的配准问题,提出了一种全新的快速点特征直方图(FPFH)特征描述与Delaunay三角剖分相结合的三维点云配准方法。首先采用FPFH综合描述特征信息,通过Delaunay三角网建立特征信息的局部关联性;再根据特征点对的对应关系进行采样一致性初始变换,实现初始配准;最后,根据得到的初值采用迭代最近点法进行精确配准,获得精确转换关系。分别对简单目标物体及复杂目标物体进行配准实验。实验结果表明,将FPFH特征描述与Delaunay三角剖分结合引入传统点云配准,简化了特征提取复杂度,缩小了特征点对匹配的搜索范围,提升了配准精度及速度,实现对目标物体高效配准,对提高机器视觉特征点匹配效率具有一定指导作用。

To address the computer vision-based registration problems, this study proposes a new three-dimensional point cloud registration algorithm that combines fast point feature histogram (FPFH) feature description with Delaunay triangulation. First, the FPFH is used to comprehensively describe feature information; then, the local correlation of feature information is established using the Delaunay triangulation. Thereafter, according to the corresponding relation of point pair features, the initial conversion of the sampling consistency is performed to implement initial registration. Finally, the iterative closest point method based on the initial values is used for accurate registration to obtain a precise conversion relation. The registration experiments are conducted on simple and complex target objects. Results reveal that traditional point cloud registration can be improved by combining FPFH feature description and Delaunay triangulation. This registration simplifies the feature extraction complexity, reduces the search range of matching feature points, improves the registration speed and accuracy, achieves an efficient registration of target objects, and considerably improves the efficiency of matching feature points in machine vision.