摘要
在点云配准过程中,针对迭代最近点(ICP)算法对点云初始位置依赖性强且迭代速度慢的问题,提出一种基于尺度不变特征变换(SIFT)特征点结合ICP的点云配准方法。首先利用SIFT算法提取待配准点云和目标点云的特征点;接着计算出特征点的快速点特征直方图(FPFH)特征;然后依据该特征使用采样一致性初始配准(SAC-IA)算法求出初始变换矩阵,从而完成初始配准;最后在初始配准的基础上利用ICP算法对两片点云进行精配准。实验表明,与ICP算法相比,该方法具有较好的配准精度,同时效率也有明显的提升。
In the process of point cloud registration,the iterative closest point(ICP)algorithm is highly dependent on the initial position of point cloud and the iteration speed is slow.To solve the problem,a point cloud registration method based on scale invariant feature transform(SIFT)feature points combined with ICP is proposed.First of all,the SIFT algorithm is used to extract the feature points of the point cloud to be registered and the target point cloud;and the fast point feature histogram(FPFH)features of the feature points are calculated;afterwards,according to the feature and using sample consensus initial alignment(SAC-IA)algorithm,the initial transformation matrix is found,thus completing the initial registration;finally,on the basis of the initial registration,the ICP algorithm is used to precisely register the two point clouds.Experiments show that compared with the ICP algorithm,this method has better registration accuracy,while the efficiency is also significantly improved.
作者
荆路
武斌
方锡禄
JING Lu;WU Bin;FANG Xi-lu(College of Computer and Information Engineering,Tianjin Chengjian University,Tianjin 300384,China)
出处
《激光与红外》
CAS
CSCD
北大核心
2021年第7期944-950,共7页
Laser & Infrared
基金
国家高技术研究发展计划(863计划)项目(No.2015BAF09B02-3)资助。
作者简介
荆路(1991-),男,硕士研究生,主要研究方向为嵌入式系统、点云数据处理,E-mail:2481722020@qq.com。
