Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2017, Vol. 51 Issue (10): 1920-1927    DOI: 10.3785/j.issn.1008-973X.2017.10.005
自动化技术     
适应性距离函数与迭代最近曲面片精细配准
张梅, 彭星星
贵州财经大学 信息学院, 贵州 贵阳 550025
Fine registration with adaptive distance function and iterative closest surface
ZHANG Mei, PENG Xing-xing
Information Institute, Guizhou University of Finance and Economics, Guiyang 550025, China
 全文: PDF(6803 KB)   HTML
摘要：

针对复杂曲面物体多视角激光扫描点云数据，提出从深度图像到完整几何模型的配准方法.根据空间点相对位置在刚体变换下的不变特性，利用曲率不变特征和归一化零均值互相关系数构造有效的初始匹配点对数组.基于单位四元数对匹配的特征点对进行坐标变换求解，完成数据粗略配准.探讨改正系数的确定方法与步骤，计算不同改正系数下的均值误差，得到最佳改正系数.运用适应性距离函数和改进迭代最近曲面片精细匹配技术，将不同视角点云在三维空间进行最优化匹配.根据匹配结果计算配准误差，对配准精度和速度进行统计分析.数值试验结果表明，该方法在保证配准精度的前提下能够有效地提高配准效率.
Abstract:

A registration method from three-dimensional depth image to three-dimensional geometric models was proposed aiming at laser scanning point cloud data from multi angle of the complex curved surface object. The effective initial matching points array was constructed with curvature invariant features and normalized zero-mean cross-correlation coefficient (NZCC)according to the invariant characteristics of the relative position of the space points in the condition of rigid body transformation. The coordinate transformation of the matching feature points was solved based on the unit quaternion. Then the data coarse registration was completed. The method and procedure for determining the modified coefficient were discussed, and mean error of different correction factors was calculated in order to get the best modified coefficient. The different perspectives of clouds were optimally matched in three-dimensional space by using fine matching technology based on adaptive distance function and the improved iterative closest surface. The registration error was calculated according to the matching results, and the registration accuracy and speed were analyzed. The numerical experiments results show that the method can effectively improve the efficiency of registration in the guarantee of the registration accuracy.
收稿日期: 2016-08-01 出版日期: 2017-09-27
CLC:  TP391  
基金资助:

国家自然科学地区基金资助项目（41261094）；贵州省科技厅科学技术基金资助项目（黔科合基础1020）；贵州省教育厅自然科学拔尖人才基金资助项目（黔教合KY字069）.
作者简介: 张梅(1974-),女,教授,从事数字摄影测量与计算机视觉研究.ORCID:0000-0002-9620-6315.E-mail:zm_gy@sina.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　

引用本文:

张梅, 彭星星. 适应性距离函数与迭代最近曲面片精细配准[J]. 浙江大学学报(工学版), 2017, 51(10): 1920-1927.

ZHANG Mei, PENG Xing-xing. Fine registration with adaptive distance function and iterative closest surface. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(10): 1920-1927.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2017.10.005    或    http://www.zjujournals.com/eng/CN/Y2017/V51/I10/1920

[1] 左超,鲁敏,谭志国,等.一种新的点云拼接算法[J].中国激光,2012,39(12):1212004-1-1212004-8. ZUO Chao, LU Min, TAN Zhi-guo, et al. A novel algorithm for registration of point clouds[J]. Chinese Journal of Lasers, 2012, 39(12):1212004-1-1212004-8.
[2] SAHILLIOGLU Y, YEMEZ Y. Coarse-to-fine surface reconstruction from silhouettes and range data using mesh deformation[J].Computer Vision and Image Understanding, 2010, 114(3):334-348.
[3] BESL P J, MCKAY N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-256.
[4] CHOW C K, TSUI H T, LEE T. Surface registrationusing a dynamic genetic algorithm[J]. Pattern Recognition, 2004, 37(1):105-117.
[5] 林洪彬,刘彬,张玉存.逆向工程中散乱点云变尺度配准算法研究[J].机械工程学报,2011, 47(14):1-6. LIN Hong-bin, LIU Bin, ZHANG Yu-cun. Research on a variable scale registration algorithm for scattered point clouds in reverse engineering[J]. Journal of Mechanical Engineering, 2011, 47(14):1-6.
[6] 秦绪佳,王建奇,郑红波,等.三维不变矩特征估计的点云拼接[J].机械工程学报,2013,49(1):129-134. QIN Xu-jia, WANG Jian-qi, ZHENG Hong-bo, et al. Point clouds registration of 3D moment invariant feature estimation[J]. Journal of Mechanical Engineering, 2013, 49(1):129-134.
[7] 王蕊,李俊山,刘玲霞,等.基于几何特征的点云配准算法[J].华东理工大学学报,2009,35(5):768-773. WANG Rui, LI Jun-shan, LIU Ling-xia, et al. Registration of point clouds based on gemotric properties[J]. East China University of Science and Technology, 2009, 35(5):768-773.
[8] BASDOGAN C, OZTIRELI A C. A new feature based method for robust and efficient rigid-body registration of overlapping point clouds[J]. The Visual Computer, 2008, 24(7-9):679-688.
[9] 高鹏东,彭翔,李阿蒙,等.ICP框架下基于表面间平均体积测度的深度像配准[J].计算机辅助设计与图形学学报,2007,19(6):719-724. GAO Peng-dong, PENG Xiang, LI A-meng, et al. Range image registration with ICP frame using surface mean inter-space measure[J]. Journal of Computer-Aided Design and Computer Graphics, 2007, 19(6):719-724.
[10] 陈维桓.微分几何[M].北京:北京大学出版社,2006:77-184.
[11] ZHANG Mei, WEN Jing-hua, FAN Yong-long. A new registration method for scattered point clouds from multi-views[J]. Information Technology Journal, 2013, 12(19):5005-5010.
[12] 孙龙祥,程义民,王以孝,等.深度图像分析[M].北京:电子工业出版社,1996.
[13] 张梅,文静华,张祖勋,等.基于欧氏距离测度的激光点云配准[J].测绘科学,2010,35(3):5-8. ZHANG Mei, WEN Jing-hua, ZHANG Zu-xun, et al. Laser points cloud registration using Euclid distance measure[J]. Science of Surveying and Mapping, 2010, 35(3):5-8.
[14] POTTMANN H, HUANG Q X, YANG Y L, et al. Geometry and convergence analysis of algorithms for registration of 3D shapes[J]. International Journal of Computer Vision, 2006, 67(3):277-296.
[15] 刘宇,熊有伦.基于有界k-d树的最近点搜索算法[J].华中科技大学学报:自然科学版,2008,36(7):73-76. LIU Yu, XIONG You-lun. Algorithm for searching nearest-neighbor based on the bounded k-d tree[J]. Journal of Huazhong University of Science and Technology:Natural Science Edition, 2008, 36(7):73-76.
[16] 李文龙.复杂曲面零件数据拼合与精密加工技术研究[D].武汉:华中科技大学,2010. LI Wen-long. Research on the data registration and precision machining technologies towards complex surfaces[D]. Wuhan:Huazhong University of Science and Technology, 2010.
[17] PRESS W H, TEUKOLSKY S A, VWTTERLING W T, et al. Numerical recipes in C:the art of scientific computing[M]. 2nd ed. Cambridge:Cambridge University Press, 1992.
[18] 薛耀红,梁学章,马婷,等.扫描点云的一种自动配准方法[J].计算机辅助设计与图形学学报,2011, 23(2):223-231. XUE Yao-hong, LIANG Xue-zhang, MA Ting, et al. An automatic registration method of scanned point clouds[J]. Journal of Computer-Aided Design and Computer Graphics, 2011, 23(2):223-231.
[1] 韩勇, 宁连举, 郑小林, 林炜华, 孙中原. 基于社交信息和物品曝光度的矩阵分解推荐[J]. 浙江大学学报(工学版), 2019, 53(1): 89-98.
[2] 郑洲, 张学昌, 郑四鸣, 施岳定. 基于区域增长与统一化水平集的CT肝脏图像分割[J]. 浙江大学学报(工学版), 2018, 52(12): 2382-2396.
[3] 赵丽科, 郑顺义, 王晓南, 黄霞. 单目序列的刚体目标位姿测量[J]. 浙江大学学报(工学版), 2018, 52(12): 2372-2381.
[4] 何杰光, 彭志平, 崔得龙, 李启锐. 局部维度改进的教与学优化算法[J]. 浙江大学学报(工学版), 2018, 52(11): 2159-2170.
[5] 李志, 单洪, 马涛, 黄郡. 基于反向标签传播的移动终端用户群体发现[J]. 浙江大学学报(工学版), 2018, 52(11): 2171-2179.
[6] 王硕朋, 杨鹏, 孙昊. 听觉定位数据库构建过程优化[J]. 浙江大学学报(工学版), 2018, 52(10): 1973-1979.
[7] 魏小峰, 程承旗, 陈波, 王海岩. 基于独立边数的链码方法[J]. 浙江大学学报(工学版), 2018, 52(9): 1686-1693.
[8] 陈荣华, 王鹰汉, 卜佳俊, 于智, 高斐. 基于KNN算法与局部回归的网站无障碍采样评估[J]. 浙江大学学报(工学版), 2018, 52(9): 1702-1708.
[9] 张承志, 冯华君, 徐之海, 李奇, 陈跃庭. 图像噪声方差分段估计法[J]. 浙江大学学报(工学版), 2018, 52(9): 1804-1810.
[10] 刘洲洲, 李士宁, 李彬, 王皓, 张倩昀, 郑然. 基于弹性碰撞优化算法的传感云资源调度[J]. 浙江大学学报(工学版), 2018, 52(8): 1431-1443.
[11] 王勇超, 祝凯林, 吴奇轩, 鲁东明. 基于局部渲染的高精度模型自适应展示技术[J]. 浙江大学学报(工学版), 2018, 52(8): 1461-1466.
[12] 孙念, 李玉强, 刘爱华, 刘春, 黎威威. 基于松散条件下协同学习的中文微博情感分析[J]. 浙江大学学报(工学版), 2018, 52(8): 1452-1460.
[13] 郑守国, 崔雁民, 王青, 杨飞, 程亮. 飞机装配现场数据采集平台设计[J]. 浙江大学学报(工学版), 2018, 52(8): 1526-1534.
[14] 毕晓君, 王朝. 基于超平面投影的高维多目标进化算法[J]. 浙江大学学报(工学版), 2018, 52(7): 1284-1293.
[15] 张廷蓉, 滕奇志, 李征骥, 卿粼波, 何小海. 岩心三维CT图像超分辨率重建[J]. 浙江大学学报(工学版), 2018, 52(7): 1294-1301.