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收稿：2017-02-16，
修回：2017-9-21，
纸质出版：2018-04-16
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目的
2
直接基于点云数据本身的拼合算法对点云模型的位置和重叠度有着较高的要求。为了克服这种缺陷，提出一种针对散乱点云的分步拼合算法。
方法
2
不同于大多数已有的基于曲率信息的拼合算法，本文算法包含了一个序贯式的匹配点对筛选过程和一个基于霍夫变换的坐标变换参数估计过程。在筛选过程中，首先利用曲率相似度确定点云数据之间的初始匹配关系，然后利用刚体不变量特征邻域标识相似度以及持续特征直方图相似度对初始匹配点对进行连续两次筛选以便得到更为精确的匹配点对集。在参数估计阶段，通过对匹配点对的旋转矩阵和平移矢量的参数化处理，利用霍夫变换消除错误匹配点对对坐标变换参数估计的影响，从而得到更加准确的坐标变换参数，实现点云的3维拼合。
结果
2
利用本文算法对两片部分重叠的点云数据进行了拼接实验。实验结果表明，本文算法能很好地实现对部分重叠点云的拼合。由于霍夫变换的引入，本文算法相较于经典的Ransac算法具有更高的正确率、稳定性以及抗噪性，在运行速度上也具有一定的优越性。
结论
2
本文算法不仅能适用于任何具有任意初始相对位置的部分重叠点云的拼接，而且可以取得很高的拼合精度和很好的噪声鲁棒性。
Objective
2
Typical cloud data registration methods based on cloud data have high demands on the locations and overlapping degree of the two cloud datasets to be registered. For instance
the iterative closest point (ICP) algorithm establishes the corresponding relationship by searching the closest points
usually measured in Euclidean distance
between two point sets. Poor initial positions of two point cloud sets commonly lead to many erroneous correspondences
and eventually only local optimal solutions can be obtained rather than the global optimal solution. In addition
the ICP algorithm has high requirements on the overlapping degree between two clouds to be registered. To tackle this problem
we propose a
$${\rm step}$$
-by-
$${\rm step}$$
registration algorithm for the registration of scattered cloud data.
Method
2
Different from most existing curvature-based registration algorithms
our proposed method consists of one sequential filtering process for erroneously matched point pairs and one parameter estimation process based on the Hough transform. In the filtering process
the point cloud curvature and other geometric derivative information were calculated
and the candidate matched point pairs from different clouds were extracted subsequently on the basis of the curvature. For the further elimination of the erroneously matching point pairs
these candidates were sequentially filtered on the basis of two similarity measures for the invariant signature and the persistence feature histogram. In the parameter estimation stage
the Hough transform was used to remove the contributions of the erroneously matched point pairs and to determine the final transform for registration upon parameterizing the rotation matrices and the translation vectors of these point pairs.
Result
2
The proposed algorithm is used to register the bunny point cloud data downloaded from the official website of Stanford University for validation. To do so
the entire point cloud data are initially divided into two overlapping point cloud datasets. The overlapping degree of these two cloud datasets is approximately 22%. One of the point cloud datasets is subjected to arbitrary translation and rotation operations. Our proposed algorithm is then employed for the registration of the aforementioned two partially overlapped cloud datasets. The initial matching point set is extracted on the basis of curvature similarity
and then the similarity measures of the invariant features are used as the first filter to eliminate erroneously matched point pairs. Experimental results corroborate that approximately twice improvement in the accuracy of the matching points can be achieved upon employing the filter based on the invariant feature. Subsequently
the similarity measures of the continuous feature histogram are used as the second filter for the previously obtained matching point pairs. The results of this procedure affirm that approximately twice improvement in the average accuracy is achieved. Finally
clustering analysis is performed on the basis of the Hough transform to obtain the final coordinate transformation parameters from the aforementioned matching point pairs and to further eliminate the contribution of the outliers of the matching points to the transformation parameters. Our proposed algorithm
which used the Hough transform
is compared with the classical Ransac algorithm. The number of random sampling is set to k=1000
and the distance from the point to the space line threshold is set to 0.08 to match the parameters of the sphere space. At the 1% noise level
the matching points using the Hough algorithm accuracy is 92.67%
and the average accuracy of the Ransac algorithm is 88.2%. At the 5% noise level
50% matching accuracy can be achieved with the Hough transform-based algorithm
whereas that of the Ransac algorithm is 18.4%. Additionally
at each noise level
the results from each run of the Ransac algorithm cannot be repeated
implying that the stability of the Ransac algorithm is poor. Thus
our method outperforms the classical Ransac algorithm in accuracy
stability
and robustness to noise. Moreover
our method runs faster than that based on the Ransac algorithm.
Conclusion
2
Our proposed algorithm can be used for the registration of any partially overlapped clouds with any relative deviation and achieve high accuracy and robustness to noise. The proposed algorithm is time-consuming because it requires
$${\rm step}$$
-by-
$${\rm step}$$
implementations. Our future work will concentrate on investigating the point cloud data reduction strategy and its corresponding combination method to improve the efficiency of the algorithm further.
Chen J, Wu X J, Wang M Y, et al. Human body shape and motion tracking by hierarchical weighted ICP[C]//International Symposium on Visual Computing. Las Vegas, NV, USA: Springer-Verlag, 2011: 408-417. [ DOI:10.1007/978-3-642-24031-7_41 http://dx.doi.org/10.1007/978-3-642-24031-7_41 ]
Gutiérrez-Heredia L, D'Helft C, Reynaud E G. Simple methods for interactive 3D modeling, measurements, and digital databases of coral skeletons[J]. Limnology and Oceanography:Methods, 2015, 13(4):178-193.[DOI:10.1002/lom3.10017]
Vasiliauskas A, Šidlauskas A, Šaferis V, et al. Applications of 3D maxillary dental arch scanning for mathematical prediction of orthodontic treatment need for complete unilateral cleft lip and palate patients[J]. Electronics and Electrical Engineering, 2010, 100(4):107-112.
Haghighipanah M, Miyasaka M, Li Y M, et al. Unscented Kalman Filter and 3D vision to improve cable driven surgical robot joint angle estimation[C]//Proceedings of 2016 IEEE International Conference on Robotics and Automation. Stockholm, Sweden: IEEE, 2016: 4135-4142. [ DOI:10.1109/ICRA.2016.7487606 http://dx.doi.org/10.1109/ICRA.2016.7487606 ]
Husstedt H, Ausserlechner U, Kaltenbacher M. Precise alignment of a magnetic sensor in a coordinate measuring machine[J]. IEEE Sensors Journal, 2010, 10(5):984-990.[DOI:10.1109/JSEN.2009.2037235]
Zhu S P, Gao Y. Noncontact 3-D coordinate measurement of cross-cutting feature points on the surface of a large-scale workpiece based on the machine vision method[J]. IEEE Transactions on Instrumentation and Measurement, 2010, 59(7):1874-1887.[DOI:10.1109/TIM.2009.2030875]
Chen W Y, Tung C K, Wang C M, et al. The non-contact human-height measurement scheme[C]//Proceedings of 2011 International Conference on Machine Learning and Cybernetics. Guilin, China: IEEE, 2011: 572-575. [ DOI:10.1109/ICMLC.2011.6016821 http://dx.doi.org/10.1109/ICMLC.2011.6016821 ]
Mao Y X, Guo J P, Liang Y M, et al. Analysis of noise characteristics in an optical coherence tomographic system[J]. Acta Optica Sinica, 2005, 25(3):324-330.
毛幼馨, 郭建平, 梁艳梅, 等.低相干光断层扫描系统的噪声分析与研究[J].光学学报, 2005, 25(3):324-330. [DOI:10.3321/j.issn:0253-2239.2005.03.008]
Mohammadzade H, Hatzinakos D. Iterative closest normal point for 3D face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(2):381-397.[DOI:10.1109/TPAMI.2012.107]
Gómez-García-Bermejo J, Zalama E, Feliz R. Automated registration of 3D scans using geometric features and normalized color data[J]. Computer-Aided Civil and Infrastructure Engineering, 2013, 28(2):98-111.[DOI:10.1111/j.1467-8667.2012.00785.x]
Bucksch A, Khoshelham K. Localized registration of point clouds of botanic trees[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3):631-635.[DOI:10.1109/LGRS.2012.2216251]
Besl P J, Mckay N D. A methodfor registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-256.[DOI:10.1109/34.121791]
Chen Y, Medioni G. Object modelling by registration of multiple range images[J]. Image and Vision Computing, 1992, 10(3):145-155.[DOI:10.1016/0262-8856(92)90066-C]
Masuda T, Yokoya N. A robust method for registration and segmentation of multiple range images[C]//Proceedings of the 2nd Cad-Based Vision Workshop. Champion, PA, USA: IEEE, 1994: 106-113. [ DOI:10.1109/CADVIS.1994.284510 http://dx.doi.org/10.1109/CADVIS.1994.284510 ]
Jiang J, Cheng J, Chen X L. Registration for 3-D point cloud using angular-invariant feature[J]. Neurocomputing, 2009, 72(16-18):3839-3844.[DOI:10.1016/j.neucom.2009.05.013]
Xin W, Pu J X. Point cloud integration base on distances between points and their neighborhood centroids[J]. Journal of Image and Graphics, 2011, 16(5):886-891.
辛伟, 普杰信.点到邻域重心距离特征的点云拼接[J].中国图象图形学报, 2011, 16(5):886-891. [DOI:10.11834/jig.20110515]
Chen H, Bhanu B. 3D free-form object recognition in range images using local surface patches[J]. Pattern Recognition Letters, 2007, 28(10):1252-1262.[DOI:10.1016/j.patrec.2007.02.009]
Zhu Y J, Zhou L S, Zhang L Y. Registration of scattered cloud data[J]. Journal of Computer-Aided Design&Computer Graphics, 2006, 18(4):475-481.
朱延娟, 周来水, 张丽艳.散乱点云数据配准算法[J].计算机辅助设计与图形学学报, 2006, 18(4):475-481.[DOI:10.3321/j.issn:1003-9775.2006.04.001]
Akca D. Matching of 3D surfaces and their intensities[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62(2):112-121.[DOI:10.1016/j.isprsjprs.2006.06.001]
Chen J, Wu X J, Wang M Y, et al. 3D shape modeling using a self-developed hand-held 3D laser scanner and an efficient HT-ICP point cloud registration algorithm[J]. Optics&Laser Technology, 2013, 45:414-423.[DOI:10.1016/j.optlastec.2012.06.015]
He B W, Lin Z M, Li Y F. An automatic registration algorithm for the scattered point clouds based on the curvature feature[J]. Optics&Laser Technology, 2013, 46:53-60.[DOI:10.1016/j.optlastec.2012.04.027]
Papazov C, Burschka D. Stochastic global optimization for robust point set registration[J]. Computer Vision and Image Understanding, 2011, 115(12):1598-1609.[DOI:10.1016/j.cviu.2011.05.008]
Harada T, Kuniyoshi Y. Graphical Gaussian vector for image categorization[C]//Proceedings of the 26th Annual Conference on Neural Information Processing Systems. Lake Tahoe: NIPS, 2012: 1547-1555. http://www.mendeley.com/catalog/graphical-gaussian-vector-image-categorization/ .
Rusu R B, Blodow N, Marton Z C, et al. Aligning point cloud views using persistent feature histograms[C]//Proceedings of 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, France: IEEE, 2008: 3384-3391. [ DOI:10.1109/IROS.2008.4650967 http://dx.doi.org/10.1109/IROS.2008.4650967 ]
Yan D M, Wang W P, Liu Y, et al. Variational mesh segmentation via quadric surface fitting[J]. Computer-Aided Design, 2012, 44(11):1072-1082.[DOI:10.1016/j.cad.2012.04.005]
He M F, Zhou L S, Shen H C. Curvature estimation of scattered-point cloud data and its application[J]. Journal of Nanjing University of Aeronautics&Astronautics, 2005, 37(4):515-519.
贺美芳, 周来水, 神会存.散乱点云数据的曲率估算及应用[J].南京航空航天大学学报, 2005, 37(4):515-519. [DOI:10.3969/j.issn.1005-2615.2005.04.024]
Hoppe H, Derose T, Duchamp T, et al. Surface reconstruction from unorganized points[J]. ACM SIGGRAPH Computer Graphics, 1992, 26(2):71-78.[DOI:10.1145/142920.134011]
Eigenstetter A, Ommer B. Visual recognition using embedded feature selection for curvature self-similarity[C]//Proceedings of the Conference on Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2012: 377-385.
Perumal L. Quaternion and its application in rotation using sets of regions[J]. International Journal of Engineering and Technology Innovation, 2011, 1(1):35-52.
Fischler M A, Bolles R C. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J]. Readings in Computer Vision, 1987:726-740.[DOI:10.1016/B978-0-08-051581-6.50070-2]
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