基于分层块状全局搜索的三维点云自动配准

提出了一种分层块状全局搜索到临近点局部搜索的改进迭代最近点(ICP)算法,用于进一步提高ICP算法的配准速度并消除点云缺失对点云配准的影响。该配准方法在粗略配准之后,以点云块为分层单元对模型点集进行选取,并对选取的少量模型点进行全局搜索获取其对应最近点;然后,以这些模型点对应的最近点作为搜索中心,在场景点集中进行局部搜索,获取这些模型点的大量临近点的对应最近点;最后,剔除错误对应最近点对,并求取坐标变换。与基于KD-Tree的ICP算法和基于LS+HS(Logarithmic Search Combined with Hierarchical Model Point Selection )的ICP算法相比,该配准算法对Happy bunny扫描数据的配准速度分别提高了78%和24%;对Dragon扫描数据的配准速度分别提高了73%和30%。这些结果表明该算法可以快速、精确地实现三维点云间的配准。

Automatic 3D point cloud registration based on hierarchical block global search

A improved Iterative Closest Point(ICP) algorithm based on hierarchical block global search to neighbor local search method is presented to get up the registration speed of the ICP algorithm and remove the effect of defective point clouds on the point cloud registration. The method aims at finding the corresponding closest points for ICP algorithm and resulting in the automatic registration of 3D point clouds. After the initial registration, merely a few model points are selected hierarchically while the point cloud blocks are served as the selection units. Then, the corresponding closest points of those model points are searched globally. After a large number of neighboring points of a few model points are selected, the corresponding closest points of the vast number of the model points are searched in local areas by considering the closest points of the few model points as the searching centers. Finally, the correspondence outliers are removed, and the fine alignment transformation is obtained. As compared to both the traditional ICP algorithms based on KD-Tree and LS+HS(Logarithmic Search Combined with Hierarchical Model Point Selection), the proposed algorithm has improved its registration speeds by 78% and by 24% for the Happy bunny scanning data as well by 73% and by 30% for Dragon scanning data. It concludes that the proposed algorithm can quickly and precisely achieve the registration of 3D point clouds.