基于点云骨架及边缘轮廓的配准算法研究  

Research on Registration Algorithm Based on Point Cloud Skeleton and Edge Contour

作　　者：杨奕 鲍晨兴 郭力振[1] 张容卓 高越[1] 汪星宇 YANG Yi;BAO Chenxing;GUO Lizhen;ZHANG Rongzhuo;GAO Yue;WANG Xingyu(Beijing Aerospace Institute for Metrology and Measurement Technology,Beijing 100076,China)

机构地区：[1]北京航天计量测试技术研究所,北京100076

出　　处：《宇航计测技术》2024年第6期87-93,共7页Journal of Astronautic Metrology and Measurement

摘　　要：针对现有配准算法运算耗时长与收敛速度较慢的问题,面向天线罩工件的实测点云数据提出了一种基于点云骨架及边缘轮廓的配准算法。首先,基于体素滤波算法对点云数据进行下采样,然后基于L1中心骨架提取算法和边缘提取算法计算点云的骨架点与边缘特征点作为点云模型的关键点,最后将参考点云数据和目标点云的关键点作为配准算法的输入,基于主成分分析算法与ICP算法进行点云配准运算。通过在公开数据集Cactus和实测的天线罩点云数据进行仿真试验,表明本算法具有较高的配准精度和较好的运算性能。In response to the problems of long computation time and slow convergence speed in existing registration algorithms,a registration algorithm is proposed based on point cloud skeleton and edge contour for measured point cloud data of headgear workpieces.Firstly,point cloud data is downsampled based on voxel filtering algorithm.Then,the L1 center skeleton extraction algorithm and edge extraction algorithm are used to calculate the skeleton points and edge feature points of the point cloud as key points of the point cloud model.Finally,the reference point cloud model and target point cloud key points are used as inputs for the registration algorithm.Point cloud registration is performed based on principal component analysis algorithm and ICP algorithm.Through simulation experiments on the publicly available dataset Cactus and measured headcover point cloud data,the proposed algorithm has high registration accuracy and less computational time.

关 键 词：点云配准算法 点云骨架 边缘提取 

分 类 号：TN958[电子电信—信号与信息处理]