摘要
迭代最近点算法(Iterative closest point,ICP)因配准精度高、适应性强而被广泛使用,但是它容易受高斯噪声和离群点的影响,导致运行速度缓慢、配准精度降低,且需要两片点云具有良好的初始位置,否则会出现局部最优问题.针对以上问题,本文提出了一种新的点云配准方法,利用法向量和高斯曲率进行粗配准,去除无关点同时提供较好的初始位置,再采用基于奇异值分解的ICP算法进行精细配准,并采用斯坦福大学的点云数据集进行了配准实验,结果表明,本文算法能够有效降低高斯噪声和离群点对配准效果的干扰,改善了点云配准的运行效率和配准精度,与传统ICP算法相比,平均配准时间减少了53.5%,配准精度提高了43.2%.
Iterative closest point(ICP)algorithm is widely used due to its high registration accuracy and strong adaptability.However,it is easily affected byGaussian noise and outliers,resulting in slow running speedandreduced registration accuracy,whichrequires that the point cloud must havea good initial position,otherwise local optimization problems will occur.In view ofthe above problems,this paper proposes a new point cloud registration method,which uses coarse vector and Gaussian curvature for coarse registration,removes irrelevant points and provides a good initial position,and then uses singular value decomposition based ICP algorithm to refine registration,and use Stanford′s point cloud data to set for registration experiments.The results show that the algorithm in this paper can effectively reduce the interference of Gaussian noise and outliers on the registration effect and improve the operation efficiency and registration of point cloud registration.Compared with the traditional ICP algorithm,the average registration time is reduced by 53.5%and the registration accuracy is increased by 43.2%.
作者
石磊
严利民
SHI Lei;YAN Li-min(Microelectronics R&D Center,Shanghai University,Shanghai 200444,China)
出处
《微电子学与计算机》
北大核心
2020年第9期68-72,共5页
Microelectronics & Computer
基金
国家自然科学基金(61674100)。
关键词
迭代最近点算法
法向量
高斯曲率
粗配准
精细配准
iterative closest point algorithm
the normal vector
gaussian curvature
coarse registration
fine registration
