摘要
点云配准的质量直接影响着三维重建的质量。针对传统K-4PCS耗时长且易出现错误匹配等问题,文中提出一种基于边界质心的点云粗配准方法。通过对点云进行边界提取,既保留点云外表特征,又减少了点云数据的大小,提高了粗配准速度。为了加快边界点的提取速度,使用K-D tree算法完成对k近邻点的搜索。通过配准边界点的质心,减少点云初始距离并增加重叠度,保证了粗配准的精度。实验结果证明,文中方法在粗配准速度和精度方面都优于传统K-4PCS算法,其速度约为传统K-4PCS算法的2倍,平移和旋转精度也比传统K-4PCS高了40%以上。文中所提方法对提高点云粗配准的速度和精度具有一定的参考价值。
The quality of point cloud registration directly affects the quality of 3D reconstruction.To solve the problem that the traditional K-4PC is time-consuming and prone to mismatching,a coarse point cloud registration method based on boundary centroid is proposed.By extracting the boundary of the point cloud,the surface features of the point cloud are preserved and the size of the point cloud data is reduced,which improves the speed of coarse registration.In order to speed up the extraction of boundary points,the K-D tree algorithm is used to search for k nearest neighbors.By registering the centroid of the boundary points,the initial distance of the point cloud is reduced and the degree of overlap is increased,ensuring the accuracy of coarse registration.The experimental results show that the proposed method is better than the traditional K-4PCS algorithm in terms of speed and accuracy.The speed of this method is about twice that of traditional K-4PCS.Both the translation and rotation accuracy are 40%higher than that of traditional K-4PCS.The proposed method has certain reference value for improving the speed and accuracy of point cloud coarse registration.
作者
陆尚鸿
李文国
LU Shanghong;LI Wenguo(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电子科技》
2022年第4期53-59,66,共8页
Electronic Science and Technology
基金
国家自然科学基金(51865020)。
关键词
点云配准
粗配准
快速配准
边界提取
k近邻点
边界质心
K-4PCS
K-D
tree
point cloud registration
coarse registration
fast registration
boundary extraction
k-nearest neighbors
boundary centroid
K-4PCS
K-D tree
