用于多视点云拼接的改进ICP算法  

Improved ICP Algorithm for Multi-View Point Cloud Splicing

作　　者：陈金广[1] 郭秋梦 马丽丽[1] 徐步高[1] 

机构地区：[1]西安工程大学计算机科学学院,西安710048

出　　处：《计算机系统应用》2018年第1期180-184,共5页Computer Systems & Applications

基　　金：中国纺织工业联合会科技指导性项目计划(2017058);陕西省教育厅科研计划项目(17JK0329)

摘　　要：点云拼接在三维物体重建中有着广泛的应用,由于扫描设备会受到光照、遮挡或物体尺寸等的影响,使得扫描设备不能在同一视角下获取待测物体的全部点云信息.针对迭代最近点算法(ICP)受点云初始位姿影响较大,鲁棒性差的特点,提出一种将多视点云数据作为研究对象,基于改进ICP算法的点云拼接算法.该算法在选取特征点时,将坐标轴与阈值相结合,设定一个阈值约束候选点的搜索范围,然后得到欧氏距离最近的点集,并使用ICP算法进行点云拼接.实验结果表明使用本文算法较传统ICP算法在迭代耗时、拼接精度上有明显的优势.Point cloud splicing has a wide application in the three-dimensional object reconstruction. The scanning equipment may be limited by light, occlusion or object size, so that the scanning equipment cannot obtain all point cloud information of the object from the same angle. The accuracy of traditional ICP is influenced by the initial pose of the cloud with poor robustness. Aiming at this problem, this paper proposes a point cloud stitching algorithm with multi-view cloud data. When the feature points are selected, the coordinate axes are combined with the thresholds to set the search range of a threshold constraint candidate point, and the nearest point set of Euclidean distance is obtained. The point cloud stitching is carried out by ICP algorithm. The experimental results show that the algorithm is superior to the traditional ICP in time consuming, and the splicing accuracy has obvious advantages.

关 键 词：点云拼接 多视点云 KINECT ICP算法 三维重建 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]