| 双维度交叉特征点协同匹配的点云拼接算法 |
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| 引用本文： | 陈毅,杨海马,刘瑾,李筠,虞梓豪,潘骏,夏季.双维度交叉特征点协同匹配的点云拼接算法[J].激光与光电子学进展,2021,58(2):55-67. |
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| 作者姓名： | 陈毅 杨海马 刘瑾 李筠 虞梓豪 潘骏 夏季 |
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| 作者单位： | 上海理工大学光电信息与计算机工程学院,上海200093;上海工程技术大学电子电气工程学院,上海201620;第二军医大学长海医院,上海200433 |
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| 基金项目： | 国家自然科学基金青年科学基金(61701296);天文联合基金(U1831133);上海航天科技创新基金(SAST2017-062);上海市自然科学基金(17ZR1443500);宝山区科技创新专项基金(17-C-21);上海理工大学医工交叉项目(2019GD10)。 |
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| 摘 要： | 为提高结构光三维重构系统的点云匹配速度及精度,提出二维视图及三维点云交叉特征点协同匹配的方法.首先,通过投影变换及维度映射关系实现待拼接投影图像的归一化,经预处理后提取端点及分叉点作为关键点,对同类点进行三角划分及相似匹配得到初始点集,并将其映射至三维空间.其次,利用kd-tree搜索得到双邻域质心,根据三点构成的三角...
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| 关 键 词： | 图像处理 视角变换 双维度 协同匹配 点云拼接 迭代最近点算法 |
Point-Cloud Splicing Algorithm for Collaborative Matching of Two-Dimensional Cross Feature Points |
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| Chen Yi,Yang Haima,Liu Jin,Li Jun,Yu Zihao,Pan Jun,Xia Ji.Point-Cloud Splicing Algorithm for Collaborative Matching of Two-Dimensional Cross Feature Points[J].Laser & Optoelectronics Progress,2021,58(2):55-67. |
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| Authors: | Chen Yi Yang Haima Liu Jin Li Jun Yu Zihao Pan Jun Xia Ji |
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| Institution: | (School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Changhai Hospital Second Military,Medical University,Shanghai 200433,China) |
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| Abstract: | In order to improve the speed and accuracy of point cloud matching in the structured light three-dimensional reconstruction system,a collaborative matching method of two-dimensional view and three-dimensional point cloud across feature points is proposed in this work.First,the normalization of the projected images to be spliced is realized through projection transformation and dimension mapping.After preprocessing,the endpoints and bifurcation points are extracted as key points,and the similar points are triangulated and similarly matched to obtain the initial point set.The initial point set is mapped to three-dimensional space.Second,kd-tree search is used to obtain the centroid of the double neighborhood,and the point set is further screened according to the triangle similarity relationship formed by the three points.Finally,the quaternion method is used to complete the rough splicing,and then an improved iterative closest point(ICP)algorithm is used to complete the fine splicing.Experimental results show that the matching accuracy of the proposed algorithm is 98.16%,the matching time is 3 s,and the center of gravity distance error of the coarse splicing overlap area is 0.018mm.The proposed algorithm has high robustness for two-dimensional image perspective transformation,smooth texture,and uneven light. |
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| Keywords: | image processing perspective transformation two dimensions collaborative matching point cloud splicing iterative closest point algorithm |
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