邻域特征在点云配准中的应用

针对大规模散乱点云的配准,提出一种基于邻域特征的配准方法,该方法由初始配准和精确配准组成。首先,对目标点集进行加权处理,以此来有效减少匹配点对的数量;其次,在重心距离特征的基础上,增加了一个角度特征量来排除错误点对,并完成初始配准;最后,使用特征改进的迭代最近点(ICP)算法进行精确配准。实验结果表明,该方法初始配准结果良好,二次配准效果更加准确,达到了多视角点云的配准要求。

Application of neighborhood feature in point cloud registration

A new registration method of large-scale scattered point clouds based on invariant features of neighborhood was proposed,which consisted of preliminary registration and exact registration.Firstly,the target point set was weighted to reduce the amount of corresponding point-pairs efficiently.Secondly,on the basis of distance features between points and their neighborhood centroids,this paper added an additional geometric feature vector of included angle to eliminate bad point-pairs,and then the preliminary registration was completed.Finally,the Iterative Closest Point(ICP) algorithm with improved invariant feature was used to register accurately.The experimental results indicate the good results of the preliminary registration and the better results of the exact registration,which have met the requirement of registering point clouds from different viewpoints.