激光点云与光学影像配准：现状与趋势

doi:10.3724/SP.J.1047.2017.00528

摘要/Abstract

摘要：

激光点云与光学影像是2种重要的遥感数据源,二者的融合能够实现优势互补,具有应用价值。点云与影像的配准是实现二者集成应用的基础,虽然经历了多年的发展仍存在许多问题有待解决。本文首先通过建立点云与影像配准问题的数学范式,将整个配准问题划分为观测值提取、配准模型选择和参数优化3部分,深入分析各部分所面临的难点与挑战;然后对现有的点云与影像配准方法进行回顾与总结,对比分析各类方法的优缺点及适用范围;最后展望了今后的发展方向进行了展望,为后续的研究提供参考。

关键词: 光学影像, 激光探测与测距, 点云, 配准, 综述

Abstract:

LiDAR point cloud and optical imagery are different types of remote sensing data source. They have some unique merits, respectively, that are complementary to each other. Integrating these two dataset has significant value in many applications. However, as the existence of various error sources, point cloud and optical imagery are usually misaligned. For the purpose of further integrated processing, the registeration of point cloud and imagery is a preliminary step which will align them into a unified geo-reference frame. Although after decades of research, this registration problem is far from solved. This paper gave a detailed survey of registration between point cloud and optical images. To obtain thorough understanding of this problem, a general mathematical paradigm for the registration was established firstly. By analyzing the mathematical paradigm, we indicated three main difficulties in this registration problem, and then definitely divided the whole workflow of registration into three key parts which are named: （1） the acquisition of corresponding observations, （2） the selection of transformation models; （3） the optimization of unknowns. Afterwards, we reviewed a series of representative registration methods from the above three aspects. In the acquisition of corresponding observations, the existing methods were classified into area-based method, feature-based method and multiple-view geometry based method. In the stage of transformation models selection, frequently-used models were classified into sensor-based models and empirical models. In the unknowns’ optimization part, two principal optimization methods termed local optimization and global optimization were introduced and the general usages of these optimization in registration were described. Furthermore, we summarized the mentioned registration methods and gave a detailed comparison and analysis including the advantages / shortcomings and the applicable scope. At last, the trends of registration development were forecasted.

Key words: optical image, LiDAR, point cloud, registration, literature review

张靖, 江万寿. 激光点云与光学影像配准：现状与趋势[J]. 地球信息科学学报, 2017, 19(4): 528-539.DOI:10.3724/SP.J.1047.2017.00528

ZHANG Jing,JIANG Wanshou. Registration between Laser Scanning Point Cloud and Optical Images: Status and Trends[J]. Journal of Geo-information Science, 2017, 19(4): 528-539.DOI:10.3724/SP.J.1047.2017.00528

图/表 3

表1

表2

表3

参考文献 80

[1]	张帆,黄先锋,李德仁.激光扫描与光学影像数据配准的研究进展[J].测绘通报,2008(2):7-10.
	[Zhang F, Huang X F, Li D R. A review of registration of laser scanner data and optical image[J]. Bulletin of Surveying and Mapping, 2008(2):7-10. ]
[2]	Mishra R K, Zhang Y.A review of optical imagery and airborne LiDAR data registration methods[J]. The Open Remote Sensing Journal, 2012,6(5):54-63. doi: 10.2174/1875413901205010054
[3]	Stamos I.Automated registration of 3D-range with 2D-color images: an overview[C]. Information Sciences and Systems (CISS), 2010 44th Annual Conference on, 2010:1-6.
[4]	G. Sindhu Madhuri.Classification of image registration techniques and algorithms in digital image processing: A research survey[J]. International Journal of Computer Trends and Technology (IJCTT), 2014,15(2):78-82. doi: 10.14445/22312803/IJCTT-V15P118
[5]	Gendrin C, Markelj P, Pawiro S A, et al.Validation for 2D/3D registration II: The comparison of intensity- and gradient-based merit functions using a new gold standard data set[J]. Medical physics, 2011,38(3):1491-1502. doi: 10.1118/1.3553403
[6]	Umeda K, Godin G, Rioux M.Registration of range and color images using gradient constraints and range intensity images[C]. Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 2004,13(3):12-15.
[7]	Duraisamy P, Belkhouche Y, Jackson S, et al. Automated two-dimensional-threedimensional registration using intensity gradients for three-dimensional reconstruction[J]. Journal of Applied Remote Sensing, 2012,6(1):063517(1-13). doi: 10.1117/1.JRS.6.063517
[8]	叶沅鑫,单杰,彭剑威,等.利用局部自相似进行多光谱遥感图像自动配准[J].测绘学报,2014,43(3):268-275.
	[Ye Y X, Shan J, Peng J W.Automated multispectral remote sensing image registration based on local self-similarity[J]. Acta Geodaetica et Cartographica Sinica, 2014,43(3):268-275. ]
[9]	Kuglin C D, Hines D C.The phase correlation image alignment method[C]. Proc. IEEE 1975 Inf. Conf. Cybernet. Society, New York, 1975:163-165.
[10]	Reddy B S, Chatterji B N.An FFT-based technique for translation, rotation, and scale-invariant image registration[J]. Image Processing, IEEE Transactions on, 1996,5(8):1266-1271.
[11]	Shorter N, Kasparis T.Autonomous registration of LiDAR data to single aerial image[C]. Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International, IEEE, 2008,5:216-219.
[12]	Wong A, Orchard J.Efficient FFT-Accelerated approach to invariant optical-LIDAR registration[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2008,46(11):3917-3925. doi: 10.1109/TGRS.2008.2001685
[13]	Foroosh H, Zerubia J B, Berthod M.Extension of phase correlation to subpixel registration[J]. Image Processing, IEEE Transactions on, 2002,11(3):188-200. doi: 10.1109/83.988953 pmid: 18244623
[14]	Yan H, Liu J G.Robust phase correlation based feature matching for image co-registration and DEM generation[J]. Remote Sensing and Spatial Information Sciences, 2008,37:1751-1756.
[15]	Pluim J P, Maintz J A, Viergever M A.Mutual-information-based registration of medical images: A survey[J]. Medical Imaging, IEEE Transactions on, 2003,22(8):986-1004. doi: 10.1109/TMI.2003.815867 pmid: 12906253
[16]	Chen H M, Varshney P K, Arora M K.Performance of mutual information similarity measure for registration of multitemporal remote sensing images[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2003,41(11):2445-2454. doi: 10.1109/TGRS.2003.817664
[17]	Cole-Rhodes A, Johnson K L, LeMoigne J, et al. Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient[J]. Image Processing, IEEE Transactions on, 2003,12(12):1495-1511. doi: 10.1109/TIP.2003.819237 pmid: 18244705
[18]	Suri S, Reinartz P.Mutual-information-based registration of TerraSAR-X and Ikonos imagery in urban areas[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2010,48(2):939-949. doi: 10.1109/TGRS.2009.2034842
[19]	邓非,杨海关,李丽英.基于互信息的LIDAR与光学影像配准方法[J].测绘科学,2009,34(6):51-52.
	[Deng F, Yang H G, Li L Y.Mutual information based registration between lidar and optical image[J]. Science of Surveying and Mapping, 2009,34(6):51-52. ]
[20]	王蕾,林立文.基于梯度互信息的光学影像和LIDAR强度图配准[J].地理空间信息,2010,8(3):56-58. doi: 10.3969/j.issn.1672-4623.2010.03.019
	[Wang L, Lin L W.Registration of optical image and lidar intensity map by mutual information based on the gradient[J]. Geospatial Information, 2010,8(3):56-58. ] doi: 10.3969/j.issn.1672-4623.2010.03.019
[21]	闫利,索一凡,曹亮.车载激光雷达点云与全景影像的自动配准[J].测绘科学,2016,41(4):113-117,123. doi: 10.16251/j.cnki.1009-2307.2016.04.023
	[Yan L, Suo Y F, Cao L.Registration of vehicle-borne lidar point cloud and panoramic image[J]. Science of Surveying and Mapping, 2016,41(4):113-117,123. ] doi: 10.16251/j.cnki.1009-2307.2016.04.023
[22]	Mastin A, Kepner J, Fisher J.Automatic registration of LIDAR and optical images of urban scenes[J]. Cvpr: 2009 Ieee Conference on Computer Vision and Pattern Recognition, Vols 1-4, 2009:2631-2638. doi: 10.1109/CVPR.2009.5206539
[23]	Wang R, Ferrie F P, Macfarlane J.Automatic registration of mobile LiDAR and spherical panoramas[C]. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, IEEE, 2012:33-40.
[24]	Parmehr E G, Fraser C S, Zhang C, et al.Automatic registration of optical imagery with 3D LiDAR data using statistical similarity[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,88:28-40. doi: 10.1016/j.isprsjprs.2013.11.015
[25]	Mikolajczyk K, Tuytelaars T.Local invariant feature detectors: A survey[J]. Foundations and Trends® in Computer Graphics and Vision, 2007,3(3):177-280. doi: 10.1561/0600000017
[26]	Aanæs H, Dahl A, Pedersen K S.Interesting interest points[J]. International Journal of Computer Vision, 2012,97(1):18-35. doi: 10.1007/s11263-011-0473-8
[27]	Ding M, Lyngbaek K, Zakhor A.Automatic registration of aerial imagery with untextured 3D LiDAR models[C]. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, 2008:2486-2493.
[28]	张永军,熊小东,沈翔.城区机载LiDAR数据与航空影像的自动配准[J].遥感学报,2012,16(3):579-595. doi: 10.11834/jrs.20121082
	[Zhang Y J, Xiong X D, Shen X.Automatic registration of urban aerial imagery with airborne LiDAR data[J]. Journal of Remote Sensing, 2012,16(3):579-595. ] doi: 10.11834/jrs.20121082
[29]	Li N, Huang X, Zhang F, et al.Registration of aerial imagery and lidar data in desert areas using the centroids of bushes as control information[M]. Bethesda, MD, ETATS-UNIS: Photogrammetric Engineering & Remote Sensing, 2013:743-752.
[30]	González-Aguilera D, Rodríguez-Gonzálvez P, Gómez-Lahoz J.An automatic procedure for co-registration of terrestrial laser scanners and digital cameras[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009,64(3):308-316. doi: 10.1016/j.isprsjprs.2008.10.002
[31]	Altuntas C.An Experimental study on registration three-dimensional range images using range and intensity data[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011,3822:115-118.
[32]	Lowe D G.Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004,60(2):91-110. doi: 10.1023/B:VISI.0000029664.99615.94
[33]	Toth C K, Ju H, Grejner-Brzezinska D A. Experience with using SIFT for Multiple Image Domain Matching[C]. Annual Conference, Apr 2010, American Society for Photogrammetry and Remote Sensing, San Diego, CA, 2010.
[34]	Ju H, Toth C K, Grejner-Brzezinska D A. Evaluation of Mutlipe-domain imagery matching based on different feature spaces[C]. 2011 ASPRS Fall Conference, Herndon, VA, USA, 2011.
[35]	Bodensteiner C, Huebner W, Juengling K, et al.Local multi-modal image matching based on self-similarity[C]. Image Processing (ICIP), 2010 17th IEEE International Conference on, 2010:937-940.
[36]	Palenichka R M, Zaremba M B.Automatic extraction of control points for the registration of optical satellite and LiDAR images[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2010,48(7):2864-2879. doi: 10.1109/TGRS.2010.2043677
[37]	Habib A, Ghanma M, Morgan M, et al.Photogrammetric and LiDAR data registration using linear features[J]. Photogrammetric Engineering & Remote Sensing, 2005,71(6):699-707. doi: 10.14358/PERS.71.6.699
[38]	马洪超,姚春静,邬建伟.利用线特征进行高分辨率影像与LiDAR点云的配准[J].武汉大学学报·信息科学版, 2012,37(2):136-140,159.
	[Ma H C, Yao C J, Wu J W.Registration of lidar point clouds and high resolution images based on linear features[J]. Geomatics and Information Science of Wuhan University, 2012,37(2):136-140,159. ]
[39]	Yao C, Guang G.The direct registration of LiDAR point clouds and high resolution image based on linear feature by introducing an unknown parameter[C]. Int. Arch. Photogramm. Remote Sensing and Spatial Information Sciences, 2012:403-408.
[40]	徐景中,寇媛,袁芳,等.基于结构特征的机载LiDAR数据与航空影像自动配准[J].红外与激光工程,2013(12):3502-3508. doi: 10.3969/j.issn.1007-2276.2013.12.061
	[Xu J Z, Kou Y, Yuan F, et al.Auto-registration of aerial imagery and airborne lidar data based on structure feature[J]. Infrared and Laser Engineering, 2013,12:3502-3508. ] doi: 10.3969/j.issn.1007-2276.2013.12.061
[41]	Wang L, Neumann U.A robust approach for automatic registration of aerial images with untextured aerial LiDAR data[C]. IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 2009:2623-2630.
[42]	Kwak T S, Kim Y I, Yu K Y, et al.Registration of aerial imagery and aerial LiDAR data using centroids of plane roof surfaces as control information[J]. KSCE Journal of Civil Engineering September, 2006,10(5):365-370. doi: 10.1007/BF02830090
[43]	Habib A, Schenk T.A new approach for matching surfaces from laser scanners and optical scanners[J]. International Archives of Photogrammetry and Remote Sensing, 1999,32(3/W14):55-61.
[44]	Armenakis C, Gao Y, Sohn G. Co-Registration of Lidar and Photogrammetric Data for Updating Building Databases[C]."Core Spatial Databases - Updating, Maintenance and Services - from Theory to Practice", Haifa, Israel, 2010, ISPRS Archive Vol. 38, Part 4-8-3-W9: 96-100.
[45]	Michel R.Registration of airborne laser data with one aerial image[C]. ISPRS XXth congress, Istanbul, 2004,35:1682-1750.
[46]	Habib A, Aldelgawya M.Alternative procedures for the incorporation of LiDAR-derived linear and areal features for photogrammetric geo- referencing[C]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B1. Beijing 2008.
[47]	张良,马洪超,高广,等.点、线相似不变性的城区航空影像与机载激光雷达点云自动配准[J].测绘学报,2014,43(4):372-379. doi: 10.13485/j.cnki.11-2089.2014.0056
	[Zhang L, Ma H C, Gao G, et al.Automatic registration of urban aerial images with airborne lidar points based on line-point similarity invariants[J]. Acta Geodaetica et Cartographica Sinica, 2014,43(4):372-379. ] doi: 10.13485/j.cnki.11-2089.2014.0056
[48]	Zhang Y, Xiong X, Zheng M, et al.LiDAR strip adjustment using multifeatures matched with aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(2):976-987. doi: 10.1109/TGRS.2014.2331234
[49]	Leberl F, Irschara A, Pock T, et al.Point clouds: lidar versus 3D vision[J]. Photogrammetric Engineering & Remote Sensing, 2010,76(10):1123. doi: 10.14358/PERS.76.10.1123
[50]	Postolov Y, Krupnik A, McIntosh K. Registration of airborne laser data to surfaces generated by Photogrammetric means[J]. International Archives of Photogrammetry and Remote Sensing, 1999,32(3/W14):95-99.
[51]	Stamos I, Liu L Y, Chen C, et al.Integrating automated range registration with multiview geometry for the photorealistic modeling of large-scale scenes[J]. International Journal of Computer Vision, 2008,78(2-3):237-260. doi: 10.1007/s11263-007-0089-1
[52]	Zheng S, Huang R, Zhou Y.Registration of optical images with lidar data and its accuracy assessment[J]. Photogrammetric Engineering & Remote Sensing, 2013,79(8):731-741. doi: 10.14358/PERS.79.8.731
[53]	Li Y, Low K L.Automatic registration of color images to 3D geometry[C]. Proceedings of the 2009 Computer Graphics International Conference, Victoria, British Columbia, Canada, ACM, 2009:21-28.
[54]	陈驰,杨必胜,彭向阳.低空UAV激光点云和序列影像的自动配准方法[J].测绘学报,2015,44(5):518-525. doi: 10.11947/j.AGCS.2015.20130558
	[Chen C, Yang B S, Peng X Y.Automatic registration of low altitude uav sequent images and laser point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2015,44(5):518-525. ] doi: 10.11947/j.AGCS.2015.20130558
[55]	Yang B, Chen C.Automatic registration of UAV-borne sequent images and LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,101:262-274. doi: 10.1016/j.isprsjprs.2014.12.025
[56]	Baltsavias E P.Airborne laser scanning: basic relations and formulas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1999,54(2):199-214. doi: 10.1016/S0924-2716(99)00015-5
[57]	McGlone J C. Manual of photogrammetry, 6th edition[M]. Bethesda: American Society for Photogrammetry and Remote Sensing Publishing, 2013.
[58]	Gneeniss A, Mills J, Miller P.In-flight photogrammetric camera calibration and validation via complementary lidar[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,100:3-13.
[59]	陈为民,聂倩,林昀. 基于罗德里格矩阵的车载激光点云与全景影像配准研究[J].测绘通报,2013(11):21-24.
	[ Chen W M, Nie Q, Lin Y.Research on registration of vehicle-borne laser point clouds and panoramic images based on lodrigues matrix transformation[J]. Bulletin of Surveying and Mapping, 2013,11:21-24. ]
[60]	盛庆红,陈姝文,费利佳,等.基于Plücker直线的机载LiDAR点云与航空影像的配准[J].测绘学报,2015,44(7):761-767. doi: 10.11947/j.AGCS.2015.20140123
	[Sheng Q H, Chen S W, Fei L J, et al.Registration of aerial image with airborne lidar data based on plücker line[J]. Acta Geodaetica et Cartographica Sinica, 2015,44(7):761-767. ] doi: 10.11947/j.AGCS.2015.20140123
[61]	王永波,杨化超,刘燕华,等.线状特征约束下基于四元数描述的LiDAR点云配准方法[J].武汉大学学报·信息科学版,2013,39(9):1057-1062.
	[Wang Y B, Yang H C, Liu Y H, et al.Linear-feature-constrained registration of lidar point cloud via quaternion[J]. Geomatics and Information Science of Wuhan University, 2013,38(9):1057-1062. ]
[62]	Hatze H.High-precision three-dimensional photogrammetric calibration and object space reconstruction using a modified DLT-approach[J]. Journal of Biomechanics, 1988,21(7):533-538. doi: 10.1016/0021-9290(88)90216-3 pmid: 3410856
[63]	宋恒嘉. 车载激光点云和光学影像的配准方法研究[J].测绘通报,2014(1):67-70. doi: 10.13474/j.cnki.11-2246.2014.0019
	[Song H J.Registration of vehicle-borne laser point cloud and optical image[J]. Bulletin of Surveying and Mapping, 2014,1: 67-70. ] doi: 10.13474/j.cnki.11-2246.2014.0019
[64]	李根,费章君,杨仕友.基于几何成像模型的鱼眼镜头图像校正算法和技术研究[J].机电工程,2013,30(10):1268-1272. doi: 10.3969/j.issn.1001-4551.2013.10.024
	[Li G, Fei Z J, Yang S Y.Fish-eye image correction method and technique based on geometrical imaging model[J]. Journal of Mechanical & Electrical Engineering, 2013,30(10):1268-1272. ] doi: 10.3969/j.issn.1001-4551.2013.10.024
[65]	熊友谊. 地面激光扫描点云与鱼眼全景影像的配准与整合研究[D].北京:中国矿业大学(北京),2014.
	[Xiong Y Y. Research on data registration and integration of terrestrial laser scanning point clouds and fisheye panoramic images[D]. Beijing: China University of Mining and Technology(Beijing), 2014. ]
[66]	Szeliski R.Image alignment and stitching: A tutorial[J]. Foundations and Trends® in Computer Graphics and Vision, 2006,2(1):1-104.
[67]	聂倩,蔡元波,林昀,等.车载激光点云与全景影像的配准研究[J].遥感信息,2014,29(1):15-18. doi: 10.3969/j.issn.1000-3177.2014.01.004
	[Nei Q, Cai Y B, Lin Y, et al.Registration of vehicle-borne laser point clouds and panoramic images[J]. Remote Sensing Information, 2014,29(1):15-18. ] doi: 10.3969/j.issn.1000-3177.2014.01.004
[68]	刘全海,鲍秀武,李楼.激光点云与全景影像配准方法研究[J].现代测绘,2016,39(1):21-25. doi: 10.3969/j.issn.1672-4097.2016.01.007
	[Liu Q H, Bao X W, Li L.Research of registration based on panoramic images and laser point cloud[J]. Modern Surveying and Mapping, 2016,39(1):21-25. ] doi: 10.3969/j.issn.1672-4097.2016.01.007
[69]	Swart A, Broere J, Veltkamp R, et al.Refined non-rigid registration of a panoramic image sequence to a LiDAR point cloud[C]. Photogrammetric Image Analysis, Springer, 2011:73-84.
[70]	张帆. 激光扫描数据三维建模中高保真度纹理重建研究[D].武汉:武汉大学,2008.
	[Zhang F.High fidelity texture reconstruction in 3d modeling from laser scanning data[D]. Wuhan: Wuhan University, 2008. ]
[71]	Levenberg K.A method for the solution of certain non-linear problems in least squares[J]. Quarterly of applied mathematics, 1944,2(2):164-168. doi: 10.1090/qam/10666
[72]	Fischler M A, Bolles R C.Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981,24(6):381-395. doi: 10.1145/358669.358692
[73]	李德仁. 利用选择权迭代法进行粗差定位[J].武汉大学学报·信息科学版,1984,9(1):46-68.
	[Li D R.Gross error location by means of the iteration method with variable weights[J]. Geomatics and Information Science of Wuhan University, 1984,9(1):46-68.]
[74]	Santamaría J, Cordón O, Damas S.A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling[J]. Computer Vision and Image Understanding, 2011,115(9):1340-1354. doi: 10.1016/j.cviu.2011.05.006
[75]	Yang J, Li H, Campbell D, et al.Go-ICP: A globally optimal solution to 3D ICP point-set registration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016,38(11):2241-2254. doi: 10.1109/TPAMI.2015.2513405 pmid: 26731638
[76]	Hel-Or Y, Hel-Or H, David E.Matching by tone mapping: Photometric invariant template matching[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2014,36(2):317-330. doi: 10.1109/TPAMI.2013.138 pmid: 24356352
[77]	Tagare H D, Rao M.Why does mutual-information work for image registration? A deterministic explanation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2015,37(6):1286-1296. doi: 10.1109/TPAMI.2014.2361512 pmid: 26357349
[78]	Gelfand N, Ikemoto L, Rusinkiewicz S, et al.Geometrically stable sampling for the ICP algorithm[C]. Fourth International Conference on 3-D Digital Imaging and Modeling, Proceedings, 2003:260-267.
[79]	Gressin A, Mallet C, Demantké J, et al.Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013,79(I-3):240-251. doi: 10.5194/isprsannals-I-3-111-2012
[80]	Rusinkiewicz S., Levoy M.Efficient variants of the ICP algorithm[C]. Third International Conference on 3-D Digital Imaging and Modeling, Proceedings, 2001:145-152.

方法		数据源	特征提取	特征描述	相似性测度	鲁棒性	配准精度	备注
基于区域的方法	空间域方法	光学影像,强度影像或DSM	不需要	不需要	SAD,SSD,NCC,LSCC	较差,对灰度差异敏感	高	速度较慢,适用于机载数据
	频率域方法	光学影像,强度影像或DSM	不需要	不需要	相位相关	对噪声和灰度差异具有一定鲁棒性	很高	计算速度快,适用于机载数据
	统计方法	光学影像,强度影像或DSM	不需要	不需要	互信息	对灰度鲁棒性好	高	需要较好的初值,适用于机载、车载和地面固定站
基于特征的方法	点特征	光学影像,强度影像或DSM	检测角点或斑点	基于邻域内的灰度或几何结构的描述子	KNN或NCC	对灰度差异和几何变形鲁棒	高	自然场景和人工场景内都存在丰富的点特征,适应范围广
	线特征	光学影像,强度影像或DSM	检测边缘,边缘编组	几何特征描述子	基于距离、角度等几何关系的判断	对灰度差异和几何变形鲁棒	高	适合城市区域,常用于车载数据和地面固定站数据
	面特征	光学影像,激光点云或DSM	光学影像上检测纹理均质区域,点云中检测平面	不需要	基于距离判断	好	低	适合进行粗配准
多视几何配准方法		光学影像序列,激光点云	光学影像序列中恢复三维信息	不需要	基于ICP的对应点提取	好	高	需要较好的初值,常于车载数据

模型	所需附加数据	精度	适用平台			适用领域
模型	所需附加数据	精度	机载	车载	地面	适用领域
共线方程	相机参数	高	*	*	*	大部分应用领域
直接线性变换	NA	较高	*	*	*	城市场景
鱼眼相机模型	相机参数	高	NA	*	*	城市或室内场景
全景成像模型	全景模型参数（某些方法需要相机参数）	较高	NA	*	*	城市或室内场景
多项式模型	NA	一般,和对应点数量和分布相关	NA	NA	*	对精度要求不高的单片配准应用
薄板样条模型	NA	高,但需要大量对应点	NA	NA	*	基于地面平台的高精度纹理映射

优化方法	适用问题	优化能力	计算效率
局部优化	适用于同名观测值已确定,仅需要对转换函数进行优化求解的情况	能够得到局部最优解,易受初值的影响	计算速度快
全局优化	能够同时优化同名观测值提取和转换函数估计	能够得到全局最优解	计算速度较慢