三维点云配准中的计算智能方法综述

武越; 苑咏哲; 向本华; 绳金龙; 雷佳熠; 胡聪颖; 公茂果; 马文萍; 苗启广

doi:10.11834/jig.220727

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三维点云配准中的计算智能方法综述
Overview of the computational intelligence method in 3D point cloud registration
2023年28卷第9期页码：2763-2787
收稿：2022-07-13，

修回：2022-09-14，

纸质出版：2023-09-16
DOI： 10.11834/jig.220727
稿件说明：

移动端阅览

武越，苑咏哲，向本华，绳金龙，雷佳熠，胡聪颖，公茂果，马文萍，苗启广. 2023. 三维点云配准中的计算智能方法综述. 中国图象图形学报， 28(09):2763-2787 DOI： 10.11834/jig.220727.

Wu Yue， Yuan Yongzhe， Xiang Benhua， Sheng Jinlong， Lei Jiayi， Hu Congying， Gong Maoguo， Ma Wenping， Miao Qiguang. 2023. Overview of the computational intelligence method in 3D point cloud registration. Journal of Image and Graphics， 28(09):2763-2787 DOI： 10.11834/jig.220727.

摘要

随着三维数据采集设备的成熟与普及，由激光雷达、结构光传感器和立体相机等设备采集的点云数据引起了广泛关注，并以此研究出针对点云的配准、分类、分割和跟踪等算法，推动了点云相关研究领域的科研进展。点云配准是点云数据处理的一个重要研究方向，旨在找到一个刚性变换运动参数，使得运动参数作用于源点云后能够与参考点云对齐。传统的点云配准方法大多存在着对初始位姿、异常值敏感等问题，而计算智能方法在处理点云配准问题，例如去除孤立点或求解矩阵时可以有效解决这些问题，并且能够适用于点云重叠程度低的情况，表现出较强的鲁棒性和泛化性。这种方法不依赖于问题本身特性，也不需要建立精确的模型，只需要求出替代真值解的近似解，大幅简化了计算量。在计算智能中，深度学习因其强大的学习能力越来越多地应用于点云配准问题，同时由于进化计算的强大全局搜索能力以及模糊逻辑对不确定性的良好适应能力，许多研究也将这两种方法应用于配准问题。本文对点云配准领域中的计算智能方法进行全面讨论，分别从基于深度学习、进化计算和模糊逻辑的点云配准方法这3个方面展开论述，同时对传统的点云配准方法进行简要介绍，并对各种方法的优缺点进行了详细讨论，旨在以更全面、清晰的方式总结点云配准问题上的相关研究。

Abstract

Point cloud data collected by lidar， structured light sensors， and stereo cameras have attracted widespread attention with the maturity and popularization of 3D data acquisition equipment. On this basis， many algorithms of point cloud registration， classification， segmentation， and tracking have been developed. Algorithms promote research progress in the field of point cloud. Point cloud registration is an important research direction in point cloud data processing. It aims to find a rigid transformation motion parameter. Thus， the motion parameter can be aligned with the reference point cloud after acting on the source point cloud. Most of the traditional point cloud registration methods are sensitive to initial poses and outliers. In comparison， computational intelligence methods can effectively solve point cloud registration problems. They can also be applied to handle the partially-overlapping problem. In these cases， computational intelligence methods show strong robustness and generalization. These methods do not depend on the characteristics of the problem itself nor require the establishment of an accurate model. However， they only require an approximate solution to replace the true value solution， thereby greatly simplifying the calculation amount. The applications of computational intelligence methods in point cloud registration have three main categories： deep learning， evolutionary computing， and fuzzy logic. The deep learning methods in point cloud registration can be divided into two types according to whether a corresponding relationship exists： the corresponding point cloud registration method and the noncorresponding point cloud registration method. The research on the former is based on the traditional iterative closest point framework； that is， the network framework is divided into four parts： feature extraction， feature matching， outlier elimination， and motion parameter estimation. In comparison， the noncorresponding point cloud registration is performed by searching for two parts. The difference in the global features of the point cloud estimates the motion parameters. Noncorresponding point cloud registration finds the difference between the global features of the two point clouds. It also solves the motion parameters according to the difference and includes two important steps： 1） extracting the global features sensitive to the pose of the point cloud and 2） using the differences in global features to solve for motion parameters. The main point cloud registration is the point cloud registration method based on the correspondence relationship. The global feature descriptor is used by proposing global registration to describe the point cloud registration. The feature descriptors are used to include the neighbors of the feature points. Domain information can effectively overcome the problem that the traditional iterative closest point method is sensitive to the initial pose and easily falls into the local minimum. The correspondence-based point cloud registration method comprises four modules： feature extraction， feature matching， outlier removal， and motion parameter estimation. Feature extraction is the primary task in point cloud registration. The quality of the extracted features directly affects global performance. In the point cloud registration based on correspondence， the global features of all points in the two point cloud sets are first extracted to generate a map. After the transformation matrix is solved， feature extraction mainly includes voxel-based feature extraction and feature extraction based on raw data. Feature matching can find the corresponding points in the overlapping area， thereby evaluating the transformation matrix. Compared with the traditional point cloud， deep learning-based point cloud registration uses the network to generate the corresponding points. Outliers greatly impact the point cloud registration performance. The weights of the points can be solved through the neural network， and the corresponding points with large weights can be selected through the maximum pool for registration. The outliers can also be removed. Motion parameter estimation is the last task in point cloud registration. It solves the rotation matrix and translation vector by mainly using regression and singular value decomposition. Evolutionary computing methods in point cloud registration mainly include two categories： evolutionary algorithms and swarm intelligence. The genetic algorithm and differential evolution algorithm are mainly used in the evolutionary algorithm and point cloud registration. The genetic algorithm constructs the population， evaluates the individual， and performs crossover mutation according to the fitness to evolve the population until the population meets the termination condition and obtains the optimal solution. The differential evolution algorithm is a heuristic global search algorithm. It encodes the parameters in the point cloud registration. Then， it initializes the population， performs mutation crossover and selection according to different strategies， and finally finds the optimal transformation parameters according to the iterative search. For point cloud registration， the point cloud registration method based on swarm intelligence is also mainly divided into two types： particle swarm optimization algorithm and ant colony optimization algorithm. The particle swarm algorithm in point cloud registration first designs an appropriate objective function as a fitness function. Then， it encodes the parameters to generate an initialization particle swarm and updates the individual best position and the global best position according to fitness. It iterates until the termination condition is met. The evolutionary algorithm has robustness， parallelism， and self-adaptation. These features are in good agreement with the characteristics of point cloud registration. The fuzzy logic method in point cloud registration is mainly used in two ways： 1） reducing the number of point clouds and 2） point cloud registration based on fuzzy clustering. When the point cloud is reduced， the quality of the point cloud can be improved by dividing the point cloud input space into several fuzzy sets and defining fuzzy rules and membership functions of fuzzy variables. The fuzzy clustering method has three main steps： converting the point cloud input into a fuzzy matrix， establishing a fuzzy similarity matrix， and relying on the fuzzy matrix for classification. This method can evaluate point cloud registration quality without ground truth. The present article discusses in detail the above point cloud registration methods and the advantages and disadvantages of each method to summarize the related research on point cloud registration comprehensively and clearly.

关键词

Keywords

references

Abdel-Basset M ， Fakhry A E ， El-Henawy I ， Qiu T and Sangaiah A K . 2017 . Feature and intensity based medical image registration using particle swarm optimization . Journal of Medical Systems ， 41 （ 12 ）： # 197 ［ DOI： 10.1007/s10916-017-0846-9 http://dx.doi.org/10.1007/s10916-017-0846-9 ］

Aiger D ， Mitra N J and Cohen-Or D . 2008 . 4-points congruent sets for robust pairwise surface registration . ACM Transactions on Graphics ， 27 （ 3 ）： 1 - 10 ［ DOI： 10.1145/1360612.1360684 http://dx.doi.org/10.1145/1360612.1360684 ］

Albukhanajer W A ， Briffa J A and Jin Y C . 2015 . Evolutionary multiobjective image feature extraction in the presence of noise . IEEE Transactions on Cybernetics ， 45 （ 9 ）： 1757 - 1768 ［ DOI： 10.1109/tcyb.2014.2360074 http://dx.doi.org/10.1109/tcyb.2014.2360074 ］

Aoki Y ， Goforth H ， Srivatsan R A and Lucey S . 2019 . PointNetLK： robust and efficient point cloud registration using PointNet // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 7156 - 7165 ［ DOI： 10.1109/cvpr.2019.00733 http://dx.doi.org/10.1109/cvpr.2019.00733 ］

Back T ， Hammel U and Schwefel H P . 1997 . Evolutionary computation： comments on the history and current state . IEEE Transactions on Evolutionary Computation ， 1 （ 1 ）： 3 - 17 ［ DOI： 10.1109/4235.585888 http://dx.doi.org/10.1109/4235.585888 ］

Bai X Y ， Luo Z X ， Zhou L ， Chen H K ， Li L ， Hu Z Y ， Fu H B and Tai CL . 2021 . PointDSC：robust point cloud registration using deep spatial consistency // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Nashville， USA ： IEEE： 15859 - 15869 ［ DOI： 10.1109/cvpr46437.2021.01560 http://dx.doi.org/10.1109/cvpr46437.2021.01560 ］

Baker S and Matthews I . 2004 . Lucas-kanade 20 years on： a unifying framework . International Journal of Computer Vision ， 56 （ 3 ）： 221 - 255 ［ DOI： 10.1023/b：visi.0000011205.11775.fd http://dx.doi.org/10.1023/b：visi.0000011205.11775.fd ］

Berks G ， Ghassemi A and von Keyserlingk D G . 2001 . Spatial registration of digital brain atlases based on fuzzy set theory . Computerized Medical Imaging and Graphics ， 25 （ 1 ）： 1 - 10 ［ DOI： 10.1016/s0895-6111（00）00038-0 http://dx.doi.org/10.1016/s0895-6111（00）00038-0 ］

Bermejo E ， Valsecchi A ， Damas S and Cordón O . 2015 . Bacterial foraging optimization for intensity-based medical image registration // Proceedings of 2015 IEEE Congress on Evolutionary Computation . Sendai， Japan ： IEEE： 2436 - 2443 ［ DOI： 10.1109/cec.2015.7257187 http://dx.doi.org/10.1109/cec.2015.7257187 ］

Besl P J and McKay N D . 1992 . A method for registration of 3-D shapes . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 14 （ 2 ）： 239 - 256 ［ DOI： 10.1109/34.121791 http://dx.doi.org/10.1109/34.121791 ］

Bhuvaneshwari B and Rajeswari A . 2018 . 3D Reconstruction using artificial bee colony based iterative closest point algorithm . Journal of Intelligent and Fuzzy Systems ， 35 （ 2 ）： 1721 - 1732 ［ DOI： 10.3233/jifs-169708 http://dx.doi.org/10.3233/jifs-169708 ］

Budak I ， Sokovic M and Barisic B . 2011 . Accuracy improvement of point data reduction with sampling-based methods by fuzzy logic-based decision-making . Measurement ， 44 （ 6 ）： 1188 - 1200 ［ DOI： 10.1016/j.measurement.2011.03.026 http://dx.doi.org/10.1016/j.measurement.2011.03.026 ］

Chen Y and Medioni G . 1992 . Object modelling by registration of multiple range images . Image and Vision Computing ， 10 （ 3 ）： 145 - 155 ［ DOI： 10.1016/0262-8856（92）90066-c http://dx.doi.org/10.1016/0262-8856（92）90066-c ］

Chen Y S . 2019 . Performance identification in large-scale class data from advanced facets of computational intelligence and soft computing techniques . International Journal of High Performance Computing and Networking ， 13 （ 3 ）： 283 - 293 ［ DOI： 10.1504/ijhpcn.2019.098569 http://dx.doi.org/10.1504/ijhpcn.2019.098569 ］

Chetverikov D ， Stepanov D and Krsek P . 2005 . Robust Euclidean alignment of 3D point sets： the trimmed iterative closest point algorithm . Image and Vision Computing ， 23 （ 3 ）： 299 - 309 ［ DOI： 10.1016/j.imavis.2004.05.007 http://dx.doi.org/10.1016/j.imavis.2004.05.007 ］

Choy C ， Dong W and Koltun V . 2020 . Deep global registration // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Seattle， USA ： IEEE： 2511 - 2520 ［ DOI： 10.1109/CVPR42600.2020.00259 http://dx.doi.org/10.1109/CVPR42600.2020.00259 ］

Cocianu C L and Stan A . 2019 . New evolutionary-based techniques for image registration . Applied Sciences ， 9 （ 1 ）： # 176 ［ DOI： 10.3390/app9010176 http://dx.doi.org/10.3390/app9010176 ］

Damas S ， Cordón O and Santamaría J . 2011 . Medical image registration using evolutionary computation： an experimental survey . IEEE Computational Intelligence Magazine ， 6 （ 4 ）： 26 - 42 ［ DOI： 10.1109/mci.2011.942582 http://dx.doi.org/10.1109/mci.2011.942582 ］

Das A and Waslander S L . 2012 . Scan registration with multi-scale k-means normal distributions transform // Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . Vilamoura-Algarve， Portugal ： IEEE： 2705 - 2710 ［ DOI： 10.1109/IROS.2012.6386185 http://dx.doi.org/10.1109/IROS.2012.6386185 ］

Das A and Waslander S L . 2014 . Scan registration using segmented region growing NDT . The International Journal of Robotics Research ， 33 （ 13 ）： 1645 - 1663 ［ DOI： 10.1177/0278364914539404 http://dx.doi.org/10.1177/0278364914539404 ］

Das S ， Mullick S S and Suganthan P N . 2016 . Recent advances in differential evolution —— an updated survey . Swarm and Evolutionary Computation ， 27 ： 1 - 30 ［ DOI： 10.1016/j.swevo.2016.01.004 http://dx.doi.org/10.1016/j.swevo.2016.01.004 ］

De Falco I ， Cioppa A D ， Maisto D ， Scafuri U and Tarantino E . 2009 . Distributed differential evolution for the registration of satellite and multimodal medical imagery //Cagnoni S， ed. Evolutionary Image Analysis and Signal Processing . Berlin， Heidelberg ： Springer： 153 - 169

De Falco I ， Della Cioppa A ， Maisto D and Tarantino E . 2008 . Differential evolution as a viable tool for satellite image registration . Applied Soft Computing ， 8 （ 4 ）： 1453 - 1462 ［ DOI： 10.1016/j.asoc.2007.10.013 http://dx.doi.org/10.1016/j.asoc.2007.10.013 ］

De Jong K . 2016 . Evolutionary computation： a unified approach // Proceedings of 2016 on Genetic and Evolutionary Computation Conference Companion . Colorado， USA ： ACM： 185 - 199 ［ DOI： 10.1145/2908961.2926973 http://dx.doi.org/10.1145/2908961.2926973 ］

Delibasis K K ， Asvestas P A and Matsopoulos G K . 2011 . Automatic point correspondence using an artificial immune system optimization technique for medical image registration . Computerized Medical Imaging and Graphics ， 35 （ 1 ）： 31 - 41 ［ DOI： 10.1016/j.compmedimag.2010.09.002 http://dx.doi.org/10.1016/j.compmedimag.2010.09.002 ］

Deng H W ， Birdal T and Ilic S . 2018a . PPFNet： global context aware local features for robust 3D point matching // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Salt Lake City， USA ： IEEE： 195 - 205 ［ DOI： 10.1109/CVPR.2018.00028 http://dx.doi.org/10.1109/CVPR.2018.00028 ］

Deng H W ， Birdal T and Ilic S . 2018b . PPF-FoldNet： unsupervised learning of rotation invariant 3D local descriptors // Proceedings of the 15th European Conference on Computer Vision （ECCV） . Munich， Germany ： Springer： 620 - 638 ［ DOI： 10.1007/978-3-030-01228-1_37 http://dx.doi.org/10.1007/978-3-030-01228-1_37 ］

Deng H W ， Birdal T and Ilic S . 2019 . 3D local features for direct pairwise registration // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Long Beach， USA ： IEEE： 3239 - 3248 ［ DOI： 10.1109/CVPR.2019.00336 http://dx.doi.org/10.1109/CVPR.2019.00336 ］

Dorigo M ， Maniezzo V and Colorni A . 1996 . Ant system： optimization by a colony of cooperating agents . IEEE Transactions on Systems， Man， and Cybernetics， Part B （Cybernetics）， 26 （ 1 ）： 29 - 41 ［ DOI： 10.1109/3477.484436 http://dx.doi.org/10.1109/3477.484436 ］

Du X G ， Dang J W ， Wang Y P ， Liu X G and Li S . 2013 . An algorithm multi-resolution medical image registration based on firefly algorithm and Powell // Proceedings of the 3rd International Conference on Intelligent System Design and Engineering Applications . Hong Kong， China ： IEEE： 274 - 277 ［ DOI： 10.1109/isdea.2012.68 http://dx.doi.org/10.1109/isdea.2012.68 ］

Eiben A E and Schoenauer M . 2002 . Evolutionary computing . Information Processing Letters ， 82 （ 1 ）： 1 - 6 ［ DOI： 10.1016/s0020-0190（02）00204-1 http://dx.doi.org/10.1016/s0020-0190（02）00204-1 ］

Farhood H ， Perry S ， Cheng E and Kim J . 2020 . Enhanced 3D point cloud from a light field image . Remote Sensing ， 12 （ 7 ）： # 1125 ［ DOI： 10.3390/rs12071125 http://dx.doi.org/10.3390/rs12071125 ］

Feng Y Q ， Tang J L ， Su B H ， Su Q L and Zhou Z . 2020 . Point cloud registration algorithm based on the grey wolf optimizer . IEEE Access ， 8 ： 143375 - 143382 ［ DOI： 10.1109/access.2020.3013706 http://dx.doi.org/10.1109/access.2020.3013706 ］

Fogel D B . 1997 . The advantages of evolutionary computation //Lundh D， Olsson B and Narayanan A， eds. Biocomputing and Emergent Computation ： Proceedings of BCE-C 97 . Singapore： World Scientific： 1 - 11

Fotsing C ， Nziengam N and Bobda C . 2020 . Large common plansets-4-points congruent sets for point cloud registration . ISPRS International Journal of Geo-Information ， 9 （ 11 ）： # 647 ［ DOI： 10.3390/ijgi9110647 http://dx.doi.org/10.3390/ijgi9110647 ］

Fu K X ， Liu S L ， Luo X Y and Wang M N . 2021 . Robust point cloud registration framework based on deep graph matching // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Nashville， USA ： IEEE： 8889 - 8898 ［ DOI： 10.1109/CVPR46437.2021.00878 http://dx.doi.org/10.1109/CVPR46437.2021.00878 ］

Galantucci L M ， Percoco G and Spina R . 2004 . An artificial intelligence approach to registration of free-form shapes . CIRP Annals ， 53 （ 1 ）： 139 - 142 ［ DOI： 10.1016/s0007-8506（07）60663-5 http://dx.doi.org/10.1016/s0007-8506（07）60663-5 ］

Ge Y Q ， Wang B Y ， Nie J H and Sun B . 2016 . A point cloud registration method combining enhanced particle swarm optimization and iterative closest point method // Proceedings of 2016 Chinese Control and Decision Conference . Yinchuan， China ： IEEE： 2810 - 2815 ［ DOI： 10.1109/ccdc.2016.7531460 http://dx.doi.org/10.1109/ccdc.2016.7531460 ］

Gold S ， Rangarajan A ， Lu C P ， Pappu S and Mjolsness E . 1998 . New algorithms for 2D and 3D point matching： pose estimation and correspondence . Pattern Recognition ， 31 （ 8 ）： 1019 - 1031 ［ DOI： 10.1016/S0031-3203（98）80010-1 http://dx.doi.org/10.1016/S0031-3203（98）80010-1 ］

Groβ J ， Ošep A and Leibe B . 2019 . AlignNet-3D： fast point cloud registration of partially observed objects // Proceedings of 2019 International Conference on 3D Vision （3DV） . Quebec City， Canada ： IEEE： 623 - 632 ［ DOI： 10.1109/3DV.2019.00074 http://dx.doi.org/10.1109/3DV.2019.00074 ］

Hanke K and Schenk S . 2014 . A genetic algorithm approach for the rigorous registration of arbitrary laser scanner point clouds //Hofstetter G， ed. Computational Engineering . Cham ： Springer： 225 - 257 ［ DOI： 10.1007/978-3-319-05933-4_9 http://dx.doi.org/10.1007/978-3-319-05933-4_9 ］

Hata Y ， Kobashi S ， Hirano S and Ishikawa M . 1999 . Registration of multi-modality medical images by soft computing approach // Proceedings of the 6th International Conference on Neural Information Processing . Perth， Australia ： IEEE： 878 - 883 ［ DOI： 10.1109/iconip.1999.844653 http://dx.doi.org/10.1109/iconip.1999.844653 ］

Holland J H . 1975 . Adaptation in Natural and Artificial Systems . Cambridge， USA ： The University of Michigan Press

Horache S ， Deschaud J E and Goulette F . 2021 . 3D point cloud registration with multi-scale architecture and unsupervised transfer learning // Proceedings of 2021 International Conference on 3D Vision . London， UK ： IEEE： 1351 - 1361 ［ DOI： 10.1109/3DV53792.2021.00142 http://dx.doi.org/10.1109/3DV53792.2021.00142 ］

Huang J D ， Kwok T H and Zhou C . 2017 . V4PCS： volumetric 4PCS algorithm for global registration . Journal of Mechanical Design ， 139 （ 11 ）： # 111403 ［ DOI： 10.1115/1.4037477 http://dx.doi.org/10.1115/1.4037477 ］

Huang S Y ， Gojcic Z ， Usvyatsov M ， Wieser A and Schindler K . 2021 . PREDATOR： registration of 3D point clouds with low overlap // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Nashville， USA ： IEEE： 4265 - 4274 ［ DOI： 10.1109/CVPR46437.2021.00425 http://dx.doi.org/10.1109/CVPR46437.2021.00425 ］

Huang X S ， Mei G F and Zhang J . 2020 . Feature-metric registration： a fast semi-supervised approach for robust point cloud registration without correspondences // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Seattle， USA ： IEEE： 11363 - 11371 ［ DOI： 10.1109/cvpr42600.2020.01138 http://dx.doi.org/10.1109/cvpr42600.2020.01138 ］

Ingole V T ， Deshmukh C N ， Joshi A and Shete D . 2009 . Medical image registration using genetic algorithm // Proceedings of the 2nd International Conference on Emerging Trends in Engineering and Technology . Nagpur， India ： IEEE： 63 - 66 ［ DOI： 10.1109/ICETET.2009.143 http://dx.doi.org/10.1109/ICETET.2009.143 ］

Iqbal R ， Doctor F ， More B ， Mahmud S and Yousuf U . 2020 . Big data analytics： computational intelligence techniques and application areas . Technological Forecasting and Social Change ， 153 ： # 119253 ［ DOI： 10.1016/j.techfore.2018.03.024 http://dx.doi.org/10.1016/j.techfore.2018.03.024 ］

Ji S J ， Ren Y C ， Ji Z ， Liu X L and Hong G . 2017 . An improved method for registration of point cloud . Optik ， 140 ： 451 - 458 ［ DOI： 10.1016/j.ijleo.2017.01.041 http://dx.doi.org/10.1016/j.ijleo.2017.01.041 ］

Kachitvichyanukul V . 2012 . Comparison of three evolutionary algorithms： GA， PSO， and DE . Industrial Engineering and Management Systems ， 11 （ 3 ）： 215 - 223 ［ DOI： 10.7232/iems.2012.11.3.215 http://dx.doi.org/10.7232/iems.2012.11.3.215 ］

Karaboga D ， Gorkemli B ， Ozturk C and Karaboga N . 2014 . A comprehensive survey： artificial bee colony （ABC） algorithm and applications . Artificial Intelligence Review ， 42 （ 1 ）： 21 - 57 ［ DOI： 10.1007/s10462-012-9328-0 http://dx.doi.org/10.1007/s10462-012-9328-0 ］

Kennedy J and Eberhart R . 1995 . Particle swarm optimization // Proceedings of ICNN’95-international Conference on Neural Networks . Perth， Australia ： IEEE： 1942 - 1948 ［ DOI： 10.1109/icnn.1995.488968 http://dx.doi.org/10.1109/icnn.1995.488968 ］

Khanna K and Rajpal N . 2015 . Reconstruction of curves from point clouds using fuzzy logic and ant colony optimization . Neurocomputing ， 161 ： 72 - 80 ［ DOI： 10.1016/j.neucom.2014.11.071 http://dx.doi.org/10.1016/j.neucom.2014.11.071 ］

Khoury M ， Zhou Q Y and Koltun V . 2017 . Learning compact geometric features // Proceedings of 2017 IEEE International Conference on Computer Vision （ICCV） . Venice， Italy ： IEEE： 153 - 161 ［ DOI： 10.1109/ICCV.2017.26 http://dx.doi.org/10.1109/ICCV.2017.26 ］

Koide K ， Yokozuka M ， Oishi S and Banno A . 2021 . Voxelized GICP for fast and accurate 3D point cloud registration // 2021 IEEE International Conference on Robotics and Automation . Xi’an， China ： IEEE： 11054 - 11059 ［ DOI： 10.1109/ICRA48506.2021.9560835 http://dx.doi.org/10.1109/ICRA48506.2021.9560835 ］

Lelieveldt B P F ， van der Geest R J ， Rezaee M R ， Bosch J G and Reiber J H C . 1999 . Anatomical model matching with fuzzy implicit surfaces for segmentation of thoracic volume scans . IEEE Transactions on Medical Imaging ， 18 （ 3 ）： 218 - 230 ［ DOI： 10.1109/42.764893 http://dx.doi.org/10.1109/42.764893 ］

Li C L and Dian S Y . 2018 . Dynamic differential evolution algorithm applied in point cloud registration . IOP Conference Series： Materials Science and Engineering ， 428 ： # 012032 ［ DOI： 10.1088/1757-899x/428/1/012032 http://dx.doi.org/10.1088/1757-899x/428/1/012032 ］

Li J H ， Zhang C H ， Xu Z Y ， Zhou H N and Zhang C . 2020 . Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration // Proceedings of the 16th European Conference on Computer Vision （ECCV） . Glasgow， UK ： Springer： 378 - 394 ［ DOI： 10.1007/978-3-030-58586-0_23 http://dx.doi.org/10.1007/978-3-030-58586-0_23 ］

Li J W and Zhan J W . 2022 . Review on 3D point cloud registration method . Journal of Image and Graphics . 27 （ 2 ）： 349 - 367

李建微，占家旺 . 2022 . 三维点云配准方法研究进展 . 中国图象图形学报， 27 （ 2 ）： 349 - 367 ［ DOI： 10.11834/jig.210243 http://dx.doi.org/10.11834/jig.210243 ］

Liao Q F ， Sun D and Andreasson H . 2021 . Point set registration for 3D range scans using fuzzy cluster-based metric and efficient global optimization . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 43 （ 9 ）： 3229 - 3246 ［ DOI： 10.1109/tpami.2020.2978477 http://dx.doi.org/10.1109/tpami.2020.2978477 ］

Liu H ， Wang S L and Zhao D H . 2021 . Initial alignment for point cloud registration by improved differential evolution algorithm . Optik ， 243 ： # 166856 ［ DOI： 10.1016/j.ijleo.2021.166856 http://dx.doi.org/10.1016/j.ijleo.2021.166856 ］

Liu H B ， Liu T R ， Li Y P ， Xi M M ， Li T and Wang Y Q . 2019 . Point cloud registration based on MCMC-SA ICP algorithm . IEEE Access ， 7 ： 73637 - 73648 ［ DOI： 10.1109/access.2019.2919989 http://dx.doi.org/10.1109/access.2019.2919989 ］

Liu K and Zhang J . 2020 . Nonlinear process modelling using echo state networks optimised by covariance matrix adaption evolutionary strategy . Computers and Chemical Engineering ， 135 ： # 106730 ［ DOI： 10.1016/j.compchemeng.2020.106730 http://dx.doi.org/10.1016/j.compchemeng.2020.106730 ］

Lomonosov E ， Chetverikov D and Ek􀆦rt A . 2006 . Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm . Pattern Recognition Letters ， 27 （ 11 ）： 1201 - 1208 ［ DOI： 10.1016/j.patrec.2005.07.018 http://dx.doi.org/10.1016/j.patrec.2005.07.018 ］

Lu J ， Liu W ， Dong D L and Shao Q . 2015 . Point cloud registration algorithm based on NDT with variable size voxel // Proceedings of the 34th Chinese Control Conference . Hangzhou， China ： IEEE： 3707 - 3712 ［ DOI： 10.1109/ChiCC.2015.7260213 http://dx.doi.org/10.1109/ChiCC.2015.7260213 ］

Lu W X ， Wan G W ， Zhou Y ， Fu X Y ， Yuan P F and Song S Y . 2019 . DeepVCP： an end-to-end deep neural network for point cloud registration // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision （ICCV） . Seoul， Korea （South）： IEEE： 12 - 21 ［ DOI： 10.1109/ICCV.2019.00010 http://dx.doi.org/10.1109/ICCV.2019.00010 ］

Ma J Y ， Chen J ， Ming D L and Tian J W . 2014a . A mixture model for robust point matching under multi-layer motion . PLoS One ， 9 （ 3 ）： # 92282 ［ DOI： 10.1371/journal.pone.0092282 http://dx.doi.org/10.1371/journal.pone.0092282 ］

Ma J Y ， Zhao J ， Tian J W ， Tu Z W and Yuille A L . 2013 . Robust estimation of nonrigid transformation for point set registration // Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition （CVPR） . Portland， USA ： IEEE： 2147 - 2154 ［ DOI： 10.1109/cvpr.2013.279 http://dx.doi.org/10.1109/cvpr.2013.279 ］

Ma J Y ， Zhao J ， Tian J W ， Yuille A L and Tu Z W . 2014b . Robust point matching via vector field consensus . IEEE Transactions on Image Processing ， 23 （ 4 ）： 1706 - 1721 ［ DOI： 10.1109/TIP.2014.2307478 http://dx.doi.org/10.1109/TIP.2014.2307478 ］

Ma W P ， Fan X F ， Wu Y and Jiao L C . 2014c . An orthogonal learning differential evolution algorithm for remote sensing image registration . Mathematical Problems in Engineering ， 2014 ： # 305980 ［ DOI： 10.1155/2014/305980 http://dx.doi.org/10.1155/2014/305980 ］

Magnusson M ， Lilienthal A and Duckett T . 2007 . Scan registration for autonomous mining vehicles using 3D-NDT . Journal of Field Robotics ， 24 （ 10 ）： 803 - 827 ［ DOI： 10.1002/rob.20204 http://dx.doi.org/10.1002/rob.20204 ］

Mansour G ， Mitsi S ， Bouzakis K D ， Sagris D and Varitis E . 2010 . Developed hybrid genetic algorithm for optimizing reverse engineering methods . International Journal of Modern Manufacturing Technologies ， 2 （ 1 ）： 43 - 48

Maturana D and Scherer S . 2015 . VoxNet： a 3D convolutional neural network for real-time object recognition // Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS） . Hamburg， Germany ： IEEE： 922 - 928 ［ DOI： 10.1109/IROS.2015.7353481 http://dx.doi.org/10.1109/IROS.2015.7353481 ］

Mellado N ， Aiger D and Mitra N J . 2014 . Super 4PCS fast global pointcloud registration via smart indexing . Computer Graphics Forum ， 33 （ 5 ）： 205 - 215 ［ DOI： 10.1111/cgf.12446 http://dx.doi.org/10.1111/cgf.12446 ］

Mirjalili S ， Mirjalili S M and Lewis A . 2014 . Grey wolf optimizer . Advances in Engineering Software ， 69 ： 46 - 61 ［ DOI： 10.1016/j.advengsoft.2013.12.007 http://dx.doi.org/10.1016/j.advengsoft.2013.12.007 ］

Mohamad M ， Rappaport D and Greenspan M . 2014 . Generalized 4-points congruent sets for 3D registration // Proceedings of the 2nd International Conference on 3D Vision . Tokyo， Japan ： IEEE： 83 - 90 ［ DOI： 10.1109/3DV.2014.21 http://dx.doi.org/10.1109/3DV.2014.21 ］

Nguyen S ， Mei Y and Zhang M J . 2017 . Genetic programming for production scheduling： a survey with a unified framework . Complex and Intelligent Systems ， 3 （ 1 ）： 41 - 66 ［ DOI： 10.1007/s40747-017-0036-x http://dx.doi.org/10.1007/s40747-017-0036-x ］

Pais G D ， Ramalingam S ， Govindu V M ， Nascimento J C ， Chellappa R and Miraldo P . 2020 . 3DRegNet： a deep neural network for 3D point registration // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Seattle， USA ： IEEE： 7191 - 7201 ［ DOI： 10.1109/CVPR42600.2020.00722 http://dx.doi.org/10.1109/CVPR42600.2020.00722 ］

Panda R ， Agrawal S ， Sahoo M and Nayak R . 2017 . A novel evolutionary rigid body docking algorithm for medical image registration . Swarm and Evolutionary Computation ， 33 ： 108 - 118 ［ DOI： 10.1016/j.swevo.2016.11.002 http://dx.doi.org/10.1016/j.swevo.2016.11.002 ］

Parpinelli R S and Lopes H S . 2011 . New inspirations in swarm intelligence： a survey . International Journal of Bio-Inspired Computation ， 3 （ 1 ）： 1 - 16 ［ DOI： 10.1504/ijbic.2011.038700 http://dx.doi.org/10.1504/ijbic.2011.038700 ］

Pavlov A L ， Ovchinnikov G W ， Derbyshev D Y ， Tsetserukou D and Oseledets I V . 2018 . AA-ICP： iterative closest point with Anderson acceleration // Proceedings of 2018 IEEE International Conference on Robotics and Automation . Brisbane， Australia ： IEEE： 3407 - 3412 ［ DOI： 10.1109/ICRA.2018.8461063 http://dx.doi.org/10.1109/ICRA.2018.8461063 ］

Peng W ， Tong R F ， Qian G P and Dong J X . 2006 . A constrained ant colony algorithm for image registration // Proceedings of 2006 International Conference on Intelligent Computing . Kunming， China ： Springer： 1 - 11 ［ DOI： 10.1007/11816102_1 http://dx.doi.org/10.1007/11816102_1 ］

Porto V W . 2020 . Evolutionary programming //Baeck T， Fogel D B and Michalewicz Z， eds. ‎Evolutionary Computation 1： Basic Algorithms and Operators . Boca Raton， USA ： CRC Press： 127 - 140

Prieto P G ， Martín F ， Moreno L and Carballeira J . 2017 . DENDT： 3D-NDT scan matching with differential evolution // Proceedings of the 25th Mediterranean Conference on Control and Automation （MED） . Valletta， Malta ： IEEE： 719 - 724 ［ DOI： 10.1109/med.2017.7984203 http://dx.doi.org/10.1109/med.2017.7984203 ］

Qi C R ， Su H ， Kaichun M and Guibas L J . 2017a . PointNet： deep learning on point sets for 3D classification and segmentation // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR） . Honolulu， USA ： IEEE： 77 - 85 ［ DOI： 10.1109/CVPR.2017.16 http://dx.doi.org/10.1109/CVPR.2017.16 ］

Qi C R ， Yi L ， Su H and Guibas L J . 2017b . PointNet++： deep hierarchical feature learning on point sets in a metric space // Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach， USA ： Curran Associates Inc.： 5105 - 5114

Qin H X ， Liu Z T and Tan B Y . 2022 . Review on deep learning rigid point cloud registration . Journal of Image and Graphics ， 27 （ 2 ）： 0329 - 0348

秦红星，刘镇涛，谭博元 . 2022 . 深度学习刚性点云配准前沿进展 . 中国图象图形学报， 27 （ 2 ）： 0329 - 0348 ［ DOI： 10.11834/ jig. 210556 http://dx.doi.org/10.11834/jig.210556 ］

Ramirez L ， Durdle N G and Raso V J . 2006 . A parameters selection scheme for medical image registration // Proceedings of NAFIPS 2006—2006 Annual Meeting of the North American Fuzzy Information Processing Society . Montreal， Canada ： IEEE： 505 - 510 ［ DOI： 10.1109/nafips.2006.365461 http://dx.doi.org/10.1109/nafips.2006.365461 ］

Rezaei H ， Shakeri M ， Azadi S and Jaferzade K . 2009 . Multimodality image registration utilizing ant colony algorithm // Proceedings of the 2nd International Conference on Machine Vision . Dubai， United Arab Emirates ： IEEE： 49 - 53 ［ DOI： 10.1109/icmv.2009.21 http://dx.doi.org/10.1109/icmv.2009.21 ］

Santamaría J ， Cordón O and Damas S . 2011 . A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling . Computer Vision and Image Understanding ， 115 （ 9 ）： 1340 - 1354 ［ DOI： 10.1016/j.cviu.2011.05.006 http://dx.doi.org/10.1016/j.cviu.2011.05.006 ］

Santamaría J ， Damas S ， García-Torres J M and Cordón O . 2012 . Self-adaptive evolutionary image registration using differential evolution and artificial immune systems . Pattern Recognition Letters ， 33 （ 16 ）： 2065 - 2070 ［ DOI： 10.1016/j.patrec.2012.07.002 http://dx.doi.org/10.1016/j.patrec.2012.07.002 ］

Sarode V ， Li X Q ， Goforth H ， Aoki Y ， Srivatsan R A ， Lucey S and Choset H . 2019 . PCRNet： point cloud registration network using PointNet encoding ［EB/OL］. ［ 2021-03-03 ］. https://arxiv.org/pdf/1908.07906.pdf https://arxiv.org/pdf/1908.07906.pdf

Schenk S and Hanke K . 2009 . Genetic algorithms for automatic registration of laser scans with imperfect and subdivided features （GAReg-ISF） . Photogrammetrie Fernerkundung Geoinformation ， 2009 （ 1 ）： 23 - 32 ［ DOI： 10.1127/0935-1221/2009/0003 http://dx.doi.org/10.1127/0935-1221/2009/0003 ］

Segal A ， Haehnel D and Thrun S . 2009 . Generalized-ICP //Trinkle J， Matsuoka Y and Castellanos J A， eds. Robotics： Science and Systems V . Seattle， USA ： University of Washington ［ DOI： 10.15607/RSS.2009.V.021 http://dx.doi.org/10.15607/RSS.2009.V.021 ］

Simon D A ， Hebert M and Kanade T . 1995 . Techniques for fast and accurate intrasurgical registration . Journal of Image Guided Surgery ， 1 （ 1 ）： 17 - 29 ［ DOI： 10.3109/10929089509106822 http://dx.doi.org/10.3109/10929089509106822 ］

Slowik A and Kwasnicka H . 2020 . Evolutionary algorithms and their applications to engineering problems . Neural Computing and Applications ， 32 （ 16 ）： 12363 - 12379 ［ DOI： 10.1007/s00521-020-04832-8 http://dx.doi.org/10.1007/s00521-020-04832-8 ］

Storn R and Price K . 1995 . Differential Evolution—A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces . Technical Report TR-95- 012 . International Computer Science Institute

Su H ， Maji S ， Kalogerakis E and Learned-Miller E . 2015 . Multi-view convolutional neural networks for 3D shape recognition // Proceedings of 2015 IEEE International Conference on Computer Vision . Santiago， Chile ： IEEE： 945 - 953 ［ DOI： 10.1109/ICCV.2015.114 http://dx.doi.org/10.1109/ICCV.2015.114 ］

Shotton J ， Glocker B ， Zach C ， Izadi S ， Criminisi A and Fitzgibbon A . 2013 . Scene coordinate regression forests for camera relocalization in RGB-D images // Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition （CVPR） . Portland， USA ： IEEE： 2930 - 2937 ［ DOI： 10.1109/CVPR.2013.377 http://dx.doi.org/10.1109/CVPR.2013.377 ］

Tao L ， Nguyen T and Hasegawa H . 2016 . Global hybrid registration for 3D constructed surfaces using ray-casting and improved self-adaptive differential evolution algorithm . International Journal of Machine Learning and Computing ， 6 （ 3 ）： 160 - 166 ［ DOI： 10.18178/ijmlc.2016.6.3.592 http://dx.doi.org/10.18178/ijmlc.2016.6.3.592 ］

Tao L ， Nguyen T ， Nguyen T ， Ito T and Bui T . 2022 . An adaptive differential evolution algorithm with a point-based approach for 3D point cloud registration . Journal of Image and Graphics ， 10 （ 1 ）： 1 - 9 ［ DOI： 10.18178/joig.10.1.1-9 http://dx.doi.org/10.18178/joig.10.1.1-9 ］

Tarel J P and Boujemaa N . 1999 . A coarse to fine 3D registration method based on robust fuzzy clustering . Computer Vision and Image Understanding ， 73 （ 1 ）： 14 - 28 ［ DOI： 10.1006/cviu.1998.0673 http://dx.doi.org/10.1006/cviu.1998.0673 ］

van Laarhoven P J M and Aarts E H L . 1987 . Simulated annealing //Laarhoven P J M and Aarts E H L， eds. Simulated Annealing： Theory and Applications . Dordrecht ： Springer： 7 - 15 ［ DOI： 10.1007/978-94-015-7744-1_2 http://dx.doi.org/10.1007/978-94-015-7744-1_2 ］

Vaswani A ， Shazeer N ， Parmar N ， Uszkoreit J ， Jones L ， Gomez A N ， Kaiser Ł and Polosukhin I . 2017 . Attention is all you need // Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach， USA ： Curran Associates Inc.： 6000 - 6010

Wachowiak M P and Elmaghraby A S . 2001 . The continuous tabu search as an optimizer for 2D-to-3D biomedical image registration // Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention . Utrecht， the Netherlands ： Springer： 1273 - 1274 ［ DOI： 10.1007/3-540-45468-3_191 http://dx.doi.org/10.1007/3-540-45468-3_191 ］

Wachowiak M P ， Smolikova R ， Zheng Y F ， Zurada J M and Elmaghraby A S . 2004 . An approach to multimodal biomedical image registration utilizing particle swarm optimization . IEEE Transactions on Evolutionary Computation ， 8 （ 3 ）： 289 - 301 ［ DOI： 10.1109/tevc.2004.826068 http://dx.doi.org/10.1109/tevc.2004.826068 ］

Wang Y and Solomon J . 2019 . Deep closest point： learning representations for point cloud registration // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision （ICCV） . Seoul， Korea （South）： IEEE： 3522 - 3531 ［ DOI： 10.1109/ICCV.2019.00362 http://dx.doi.org/10.1109/ICCV.2019.00362 ］

Wongkhuenkaew R ， Auephanwiriyakul S ， Chaiworawitkul M and Theera-Umpon N . 2021a . Three-dimensional tooth model reconstruction using statistical randomization-based particle swarm optimization . Applied Sciences ， 11 （ 5 ）： # 2363 ［ DOI： 10.3390/app11052363 http://dx.doi.org/10.3390/app11052363 ］

Wongkhuenkaew R ， Auephanwiriyakul S and Theera-Umpon N . 2021b . Three-dimensional point cloud registration using particle swarm optimization // Proceedings of the 18th International Conference on Electrical Engineering/Electronics ， Computer， Telecommunications and Information Technology （ECTI-CON）. Chiang Mai ， Thailand ： IEEE： 250 - 253 ［ DOI： 10.1109/ecti-con51831.2021.9454788 http://dx.doi.org/10.1109/ecti-con51831.2021.9454788 ］

Wu Y ， Ma W P ， Miao Q G and Wang S F . 2019 . Multimodal continuous ant colony optimization for multisensor remote sensing image registration with local search . Swarm and Evolutionary Computation ， 47 ： 89 - 95 ［ DOI： 10.1016/j.swevo.2017.07.004 http://dx.doi.org/10.1016/j.swevo.2017.07.004 ］

Wu Y ， Miao Q G ， Ma W P ， Gong M G and Wang S F . 2018 . PSOSAC： particle swarm optimization sample consensus algorithm for remote sensing image registration . IEEE Geoscience and Remote Sensing Letters ， 15 （ 2 ）： 242 - 246 ［ DOI： 10.1109/lgrs.2017.2783879 http://dx.doi.org/10.1109/lgrs.2017.2783879 ］

Wu Y ， Mu G F ， Qin C ， Miao Q G ， Ma W P and Zhang X R . 2020 . Semi-supervised hyperspectral image classification via spatial-regulated self-training . Remote Sensing ， 12 （ 1 ）： # 159 ［ DOI： 10.3390/rs12010159 http://dx.doi.org/10.3390/rs12010159 ］

Wu Y ， Xiao Z L ， Liu S D ， Miao Q G ， Ma W P ， Gong M G ， Xie F and Zhang Y . 2021 . A two-step method for remote sensing images registration based on local and global constraints . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ， 14 ： 5194 - 5206 ［ DOI： 10.1109/JSTARS.2021.3079103 http://dx.doi.org/10.1109/JSTARS.2021.3079103 ］

Wu Y ， Yuan Y Z ， Yue M Y ， Gong M G ， Li H ， Zhang M Y ， Ma W P and Miao Q G . 2022 . Feature mining method of multi-dimensional information fusion in point cloud registration . Journal of Computer Research and Development ， 59 （ 8 ）： 1732 - 1741

武越，苑咏哲，岳铭煜，公茂果，李豪，张明阳，马文萍，苗启广 . 2022 . 点云配准中多维度信息融合的特征挖掘方法 . 计算机研究与发展， 59 （ 8 ）： 1732 - 1741 ［ DOI： 10.7544/issn1000-1239.20220042 http://dx.doi.org/10.7544/issn1000-1239.20220042 ］

Xu H ， Liu S C ， Wang G F ， Liu G H and Zeng B . 2021 . Omnet： learning overlapping mask for partial-to-partial point cloud registration // Proceedings of 2021 IEEE International Conference on Computer Vision （ICCV） . Montreal， Canada ： IEEE： 3132 - 3141 ［ DOI： 10.1109/iccv48922.2021.00312 http://dx.doi.org/10.1109/iccv48922.2021.00312 ］

Xu H ， Ye N J ， Liu S C ， Liu G H and Zeng B . 2022 . FINet： dual branches feature interaction for partial-to-partial point cloud registration // Proceedings of the 36th AAAI Conference on Artificial Intelligence . Vancouver， Canada ： AAAI： 2848 - 2856 ［ DOI： 10.1609/aaai.v36i3.20189 http://dx.doi.org/10.1609/aaai.v36i3.20189 ］

Xiao J ， Owens A and Torralba A . 2013 . Sun3d： a database of big spaces reconstructed using SFM and object labels // Proceedings of 2013 IEEE International Conference on Computer Vision （ICCV） . Sydney， Australia ： IEEE： 1625 - 1632 ［ DOI： 10.1109/ICCV.2013.458 http://dx.doi.org/10.1109/ICCV.2013.458 ］

Yacout G and Shoukry M K . 2021 . Reverse engineering： investigation of optimization techniques in point clouds registration . PAMM ， 20 （ 1 ）： # 202000165 ［ DOI： 10.1002/pamm.202000165 http://dx.doi.org/10.1002/pamm.202000165 ］

Yang J L ， Li H D and Jia Y D . 2013 . Go-ICP： solving 3D registration efficiently and globally optimally // Proceedings of 2013 IEEE International Conference on Computer Vision . Sydney， Australia ： IEEE： 1457 - 1464 ［ DOI： 10.1109/ICCV.2013.184 http://dx.doi.org/10.1109/ICCV.2013.184 ］

Yang X S and He X S . 2015 . Swarm intelligence and evolutionary computation： overview and analysis. Recent Advances in Swarm Intelligence and Evolutionary Computation . Cham ： Springer ： 1 - 23 ［ DOI： 10.1007/978-3-319-13826-8_1 http://dx.doi.org/10.1007/978-3-319-13826-8_1 ］

Yang Y Q ， Feng C ， Shen Y R and Tian D . 2018 . FoldingNet： point cloud auto-encoder via deep grid deformation // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Salt Lake City， USA ： IEEE： 206 - 215 ［ DOI： 10.1109/CVPR.2018.00029 http://dx.doi.org/10.1109/CVPR.2018.00029 ］

Yavari S ， Valadan Zoej M J and Salehi B . 2018 . An automatic optimum number of well-distributed ground control lines selection procedure based on genetic algorithm . ISPRS Journal of Photogrammetry and Remote Sensing ， 139 ： 46 - 56 ［ DOI： 10.1016/j.isprsjprs.2018.03.002 http://dx.doi.org/10.1016/j.isprsjprs.2018.03.002 ］

Yew Z J and Lee G H . 2020 . RPM-Net： robust point matching using learned features // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Seattle， USA ： IEEE： 11821 - 11830 ［ DOI： 10.1109/CVPR42600.2020.01184 http://dx.doi.org/10.1109/CVPR42600.2020.01184 ］

Yu Q and Wang K S . 2014 . A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method . Advances in Manufacturing ， 2 （ 1 ）： 32 - 38 ［ DOI： 10.1007/s40436-014-0059-0 http://dx.doi.org/10.1007/s40436-014-0059-0 ］

Zadeh L A . 2008 . Is there a need for fuzzy logic？ Information Sciences ， 178 （ 13 ）： 2751 - 2779 ［ DOI： 10.1016/j.ins.2008.02.012 http://dx.doi.org/10.1016/j.ins.2008.02.012 ］

Zaganidis A ， Magnusson M ， Duckett T and Cielniak G . 2017 . Semantic-assisted 3D normal distributions transform for scan registration in environments with limited structure // Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems . Vancouver， Canada ： IEEE： 4064 - 4069 ［ DOI： 10.1109/IROS.2017.8206262 http://dx.doi.org/10.1109/IROS.2017.8206262 ］

Zeng A ， Song S R ， Nieβner M ， Fisher M ， Xiao J X and Funkhouser T . 2017 . 3DMatch： learning local geometric descriptors from RGB-D reconstructions // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR） . Honolulu， USA ： IEEE： 199 - 208 ［ DOI： 10.1109/CVPR.2017.29 http://dx.doi.org/10.1109/CVPR.2017.29 ］

Zhan X ， Cai Y and He P . 2018 . A three-dimensional point cloud registration based on entropy and particle swarm optimization . Advances in Mechanical Engineering ， 10 （ 12 ）： # 1687814018814330 ［ DOI： 10.1177/1687814018814330 http://dx.doi.org/10.1177/1687814018814330 ］

Zhan X ， Cai Y ， Li H ， Li Y M and He P . 2020 . A point cloud registration algorithm based on normal vector and particle swarm optimization . Measurement and Control ， 53 （ 3/4 ）： 265 - 275 ［ DOI： 10.1177/0020294019858217 http://dx.doi.org/10.1177/0020294019858217 ］

Zhang X T ， Yang B ， Li Y H ， Zuo C L ， Wang X W and Zhang W X . 2018 . A method of partially overlapping point clouds registration based on differential evolution algorithm . PLoS One ， 13 （ 12 ）： # 0209227 ［ DOI： 10.1371/journal.pone.0209227 http://dx.doi.org/10.1371/journal.pone.0209227 ］

Zhang Z Y ， Dai Y C and Sun J D . 2020 . Deep learning based point cloud registration： an overview . Virtual Reality and Intelligent Hardware ， 3 （ 3 ）： 222 - 246 ［ DOI： 10.1016/j.vrih.2020.05.002 http://dx.doi.org/10.1016/j.vrih.2020.05.002 ］

Zhao H F ， Yao L S and Luo B . 2011 . Registration of multi-resolution medical images using a modified artificial fish-swarm algorithm combined with Powell’s method . Journal of Xi’an Jiaotong University ， 45 （ 4 ）： 46 - 52

赵海峰，姚丽莎，罗斌 . 2011 . 改进的人工鱼群算法和Powell法结合的医学图像配准 . 西安交通大学学报， 45 （ 4 ）： 46 - 52