用于多视点云拼接的改进ICP算法
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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.
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.
引文
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15 张成,汪东,沈川,等.基于奇异值分解的可分离压缩成像方法.计算机研究与发展,2016,52(12):2816–2823.[doi:10 .7544/issn1000-1239.2016.20150414]
2Chen Y,Medioni G.Object modeling by registration of multiple range images.Proceedings of 1991 IEEE International Conference on Robotics and Automation.Sacramento,CA,USA.1991.2724–2729.
3王瑞岩,姜光,高全学.结合图像信息的快速点云拼接算法.测绘学报,2016,45(1):96–102.
4杨帆,白宝兴,张振普,等.基于多视点云拼接算法研究.长春理工大学学报(自然科学版),2014,37(3):124–127.
5索俊锋,刘勇,蒋志勇,等.基于三维激光扫描点云数据的古建筑建模.测绘科学,2017,42(3):179–185.
6周朗明,郑顺义,黄荣永.旋转平台点云数据的配准方法.测绘学报,2013,42(1):73–79.
7马敏杰,杨洋,范爱平,等.复杂建筑物三维激光扫描与室内外精细建模.测绘与空间地理信息,2015,38(1):111–113.
8Wang GH,Hu ZY,Wu FC,et al.Implementation and experimental study on fast object modeling based on multiple structured stripes.Optics and Lasers in Engineering,2004,42 (6):627–638.[doi:10.1016/j.optlaseng.2004.05.008]
9李学军,杨晟,李振举,等.标识点快速提取与高精度单点匹配式定位算法.吉林大学学报(工学版),2014,44(4):1197–1202.
10袁建英,王琼,李柏林.利用标志点多视图约束实现结构光扫描高精度粗拼接.计算机辅助设计与图形学学报,2015,27 (4):674–683.
11李俊峰.大口径深焦比Hindle球面镜曲率半径精确测量.电子测量与仪器学报,2014,28(12):1401–1407.
12Zhang Y,Tao T,Mei XS,et al.An online spindle rotation error measurement system based on improved three point method.Proceedings of the 9th International Conference on Electronic Measurement&Instruments.Beijing,China.2009.651–656.
13Wyngaerd JV,van Gool L,Kock R,et al.Invariant-based registration of surface patches.The Proceedings of the 7th IEEE International Conference on Computer Vision.Kerkyra,Greece.1999.301–306.
14徐晓苏,周峰,张涛,等.基于四元数自适应卡尔曼滤波的快速对准算法.中国惯性技术学报,2016,24(4):454–459.
15 张成,汪东,沈川,等.基于奇异值分解的可分离压缩成像方法.计算机研究与发展,2016,52(12):2816–2823.[doi:10 .7544/issn1000-1239.2016.20150414]