. Demantké, de descripteurs géométriques locaux, 2012.

, appariement des points laser aux bandes du modèle 3D en utilisant un lancer de rayons, 3. acceptation et/ou rejet en utilisant les incertitudes des données en entrée, 4. une méthode d'optimisation basée sur le modèle de Gauss-Helmert (très utilisé dans le domaine de la géodésie

, Les informations initiales nécessaires au fonctionnement de la méthode sont : 1. un modèle Bati3D contenant des primitives géométriques et leur position spatiale, 2. un nuage laser provenant de véhicules de cartographie mobiles contenant : la position spatiale des points laser, la position spatiale des points constitutifs de la trajectoire d'acquisition, le temps d'acquisition de chaque point laser, 3. les incertitudes sur le modèle 3D (blocs de bâtiments, façades et bandes)

, Aucune autre information n'est nécessaire pour le recalage des données dès lors que nous connaissons les informations ci-dessus sur les données

, La méthode corrige simultanément la trajectoire du véhicule et le modèle 3D (bloc de bâtiments, façades et bandes) en propageant les incertitudes. Les diérentes évaluations permettent de garantir que les incertitudes sont bornées dans un intervalle de conance

, Prendre en compte les incertitudes permet d'éviter les choix heuristiques de paramètres dont sourait la thèse de, 2014.

, Cette approche de recalage basée sur un recalage conjoint est adaptée aux données laser mobiles/modèles 3D en milieu urbain. Cette chaîne de traitement apporte une solution à cette problématique. Cependant, L'ensemble des évaluations eectuées atteste du bon fonctionnement de notre approche et démontre un potentiel intéressant

, sur la trajectoire entraînerait une trajectoire non lisse en forme de "dents de scie

, Ouvrir la méthode aux véhicules de cartographie mobile ne remplissant pas ce critère est complexe. La gestion de l'orientation est un problème compliqué car il est dicile de faire la distinction entre une légère rotation et une légère translation

, Cette annexe est basée sur l'article de, 2011.

. En and . Shapiro, Wilk vérie l'hypothèse nulle selon laquelle un échantillon x1, ..., xn est issu d'une population normalement distribuée

, Il est basé sur la statistique W. En comparaison des autres tests, il est particulièrement puissant pour les petits eectifs, Ce test est très populaire et utilisé par de nombreux statisticiens, vol.50

, La statistique de test est la suivante : 1. trier les données x i dans l'ordre croissant, nous obtenons la série x (i) , 2. calculer les écarts (x (ni+1) x (i) ), 3. lire dans la table pour un n donné, les valeurs des coecients a i , 4. former le numérateur de W, nW , Chapter A

W. Si and . Crit,

W. Si and . Crit,

A. Figure, 1: Table des coecients a i pour le test W de Shapiro-Wilk

D. Aiger, D. Mitra, and . Cohen-or, 4-points congruent sets for robust pairwise surface registration, ACM Transactions on Graphics, vol.27, issue.3, p.19, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00622443

D. Aiger, N. Mitra, and D. Cohen-or, 4-points congruent sets for robust pairwise surface registration, ACM Transactions on Graphics, vol.27, issue.3, p.1, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00622443

D. Akca, Full automatic registration of laser scanner point clouds, Measurement Techniques, vol.1, p.330337, 2003.

M. Al-durgham, A. Detchev, and . Habib, Analysis of two triangle-based multi-surface registration algorithms of irregular point clouds, ISPRS Commission V, 2010.

K. Al-manasir and C. Fraser, Automatic registration of terrestrial laser scanner data via imagery, IAPRS, p.2631, 2006.

. B-allen, Z. Curless, and . Popovi¢, The space of human body shapes : reconstruction and parameterization from range scans, ACM Transactions on Graphics, issue.22, p.587594, 2003.

E. Majd-alshawa, P. Smigiel, T. Grussenmeyer, and . Landes, Integration of a terrestrial lidar on a mobile mapping platform-rst experiences, International Symposium on Mobile Mapping Technology, 2007.

M. A. Pinheiro, R. Sznitman, E. Serradell, J. Kybic, F. Moreno-noguer et al., Active testing search for point cloud matching, Information processing in medical imaging : 23rd International Conference, 2013.

C. A. Asilomar and . Usa, Proceedings, p.572583, 2013.

. B-amberg, T. Romdani, and . Vetter, Optimal step nonrigid icp algorithms for surface registration, IEEE Conference on Computer Vision and Pattern Recognition, vol.14, p.18, 2007.

D. Anguelov, H. Srinivasan, . Pang, S. Koller, J. Thrun et al., The correlated correspondence algorithm for unsupervised registration of nonrigid surfaces, vol.17, p.19, 2005.

. K-arun, D. Huang, and . Blostein, Least-squares tting of two 3-d point sets, Image Rochester NY, issue.5, p.19, 1987.

K. Bae and D. Lichti, Automated registration of unorganised point clouds from terrestrial laser scanners, Update, vol.35, issue.5B, p.222227, 2004.

K. Bae and D. Lichti, A method for automated registration of unorganised point clouds, ISPRS Journal of Photogrammetry and Remote Sensing, vol.63, issue.1, p.3654, 2008.

K. Baek and . Lichti, Automated registration of unorganised point cloude from terrestrial laser scanners, Update, vol.35, issue.5B, p.222227, 2004.

H. Dana and . Ballard, Generalizing the hough transform to detect arbitrary shapes, Pattern recognition, vol.13, issue.2, p.19, 1981.

S. Barnea and . Filin, Registration of terrestrial laser scans via image based features. International Archives of Photogrammetry and Remote Sensing, p.3237, 2007.

S. Barnea and . Filin, Keypoint based autonomous registration of terrestrial laser pointclouds, IJPRS, vol.63, issue.1, p.1935, 2008.

S. Barnea and S. Filin, Registration of terrestrial laser scans via image based features, 2007.

V. Barras, N. Delley, and G. Chapotte, Analyses aux limites des scanners laser terrestres, XYZ Rev. Assoc. Fr. Topogr, vol.137, p.34, 1926.

G. H. Bendels, P. Degener, R. Wahl, M. Körtgen, and R. Klein, Image-based registration of 3d-range data using feature surface elements, Computer, vol.1, p.115124, 2004.

R. Benjemaa and . Schmitt, Fast global registration of 3d sampled surfaces using a multi-z-buer technique, Image and Vision Computing, vol.17, issue.2, p.19, 1999.

R. Bergevin, H. Soucy, D. Gagnon, and . Laurendeau, Towards a general multi-view registration technique, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.5, p.540547, 1996.

F. Bernardini, I. Rushmeier, J. Martin, G. Mittleman, and . Taubin, Building a digital model of michelangelos orentine pieta, IEEE Computer Graphics and Applications, vol.22, issue.1, p.5967, 2002.

P. Besl and . Mckay, A method for registration of 3-d shapes, IEEE Transactions on PAMI, vol.14, issue.2, p.19, 1992.

P. Biber and . Strasser, The normal distributions transform : a new approach to laser scan matching, Proceedings 2003 IEEERSJ International Conference on Intelligent Robots and Systems IROS, vol.03, p.27432748, 2003.

G. Blais and M. Levine, Registering multiview range data to create 3d computer objects registering multiview range data to create 3d computer objects, Evaluation, vol.17, issue.514, p.820824, 1995.

C. Brenner, N. Dold, and . Ripperda, Coarse orientation of terrestrial laser scans in urban environments. ISPRS journal of photogrammetry and remote sensing, vol.63, p.14, 2008.

M. Thomas and . Breuel, Implementation techniques for geometric branch-and-bound matching methods, Computer Vision and Image Understanding, vol.90, issue.3, p.20, 2003.

B. Brown and . Rusinkiewicz, Global non-rigid alignment of 3-d scans, ACM Transactions on Graphics, vol.26, issue.3, p.21, 2007.

H. Bülow and A. Birk, Spectral registration of volume data for 6-dof spatial transformations plus scale, Robotics and Automation (ICRA), 2011 IEEE International Conference on, p.19, 2011.

G. Burel and H. Henocq, 3d invariants and their application to object recognition, Signal Processing, vol.45, issue.1, p.122, 1995.

B. Cannelle, Estimation de pose de grands blocs d'images panoramiques issues de systèmes de numérisation mobile, 2013.

O. Carmichael and M. Hebert, Unconstrained registration of large 3D point sets for complex model building, p.360367, 1998.

A. Censi, Scan matching in a probabilistic framework, Proceedings 2006 IEEE International Conference on, p.19, 2006.

C. Chen, J. Hung, and . Cheng, Ransac-based darces : a new approach to fast automatic registration of partially overlapping range images, IEEE Transactions on PAMI, vol.21, issue.11, p.19, 1999.

Y. Chen and G. Medioni, Object modelling by registration of multiple range images, IVC, vol.10, issue.3, p.19, 1992.

Z. Cheng, G. Jiang, . Dang, . Martin, H. Li et al., Non-rigid Registration in 3D Implicit Vector Space, p.18, 2010.

R. Chin and . Dyer, Model-based recognition in robot vision, ACM Computing Surveys, vol.18, issue.1, p.67108, 1986.

H. Chui, A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding, vol.89, p.20, 2003.

H. Chui and . Rangarajan, A new algorithm for non-rigid point matching, vol.2, p.20, 2000.

J. Civera, G. Oscar, A. J. Grasa, and . Davison, 1-point ransac for extended kalman ltering : Application to real-time structure from motion and visual odometry, Journal of Field Robotics, vol.27, issue.5, p.20, 2010.

D. Cohen, -. Steiner, and J. Morvan, Restricted delaunay triangulations and normal cycle, PAMI, vol.24, issue.8-9, p.312, 2003.

P. D-m-cole and . Newman, Using laser range data for 3d slam in outdoor environments, Proceedings 2006 IEEE International Conference on Robotics and Automation, p.15561563, 2006.

L. Cordero-grande, G. Vegas-sanchez-ferrero, P. Casaseca-de-la-higuera, and C. , A markov random eld approach for topology-preserving registration : Application to object-based tomographic image interpolation, IEEE Transactions on Image Processing, vol.21, issue.4, p.19, 2012.

J. Demantké, B. Vallet, and N. Paparoditis, Streamed Vertical Rectangle Detection in Terrestrial Laser Scans for Facade Database Production. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, p.99104, 2012.

J. Demantké, B. Vallet, and N. Paparoditis, Streamed vertical rectangle detection in terrestrial laser scans for facade database production. IAPRS, I-3 :99104, vol.10, p.17, 2012.

J. Demantké, N. Vallet, and . Paparoditis, Streamed vertical rectangle detection in terrestrial laser scans for facade database production. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.48, p.112, 2012.

M. Ding, A. Lyngbaek, and . Zakhor, Automatic registration of aerial imagery with untextured 3d lidar models. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, p.18, 2008.

H. Dirk, Probabilistic matching for 3d scan registration. Robotics, 2002.

C. Dold and C. Brenner, Registration of terrestrial laser scanning data using planar patches and image data, IAPRS, vol.36, p.7883, 2006.

C. Dorai, . Wang, C. Jain, and . Mercer, Registration and integration of multiple object views for 3d model construction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.1, p.8389, 1998.

M. Durupt and F. Taillandier, Automatic building reconstruction from a digital elevation model and cadastral data : An operational approach, 2006.

M. Durupt and . Taillandier, Reconstruction automatique de bâtimentsa partir d'un mne et de limites cadastrales : une approche opérationnelle, 2006.

D. Eggert and . Dalyot, octree-based simd strategy for icp registration and alignment of 3d point clouds, ISPRS, vol.17, p.18, 2012.

C. Ellum and N. El-sheimy, Land-based mobile mapping systems. Photogrammetric engineering and remote sensing, vol.68, p.31, 1994.

O. Enqvist, K. Josephson, and F. Kahl, Optimal correspondences from pairwise constraints, IEEE 12th International Conference on, p.19, 2009.

J. Feldmar, F. Ayache, and . Betting, 3d-2d projective registration of free-form curves and surfaces, Computer Vision and Image Understanding, vol.65, issue.3, p.403424, 1997.
URL : https://hal.archives-ouvertes.fr/inria-00074241

A. Fitzgibbon, Robust registration of 2d and 3d point sets, IVC, vol.21, p.11451153, 2003.

S. Friedman and I. Stamos, Online detection of repeated structures in point clouds of urban scenes for compression and registration, International journal of computer vision, vol.102, issue.1-3, p.112128, 2013.

. K-friston, C. Ashburner, . Frith, . Poline, R. Heather et al., Spatial registration and normalization of images, Human Brain Mapping, vol.3, issue.3, p.165189, 1995.

A. Frome, R. Huber, . Kolluri, J. Bülow, and . Malik, Recognizing objects in range data using regional point descriptors, Current, vol.1, issue.1, p.114, 2004.

C. Frueh and . Zakhor, Constructing 3d city models by merging ground-based and airborne views, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.2, p.5629, 2003.

K. Fujiwara, K. Nishino, J. Takamatsu, B. Zheng, and K. Ikeuchi, Locally rigid globally non-rigid surface registration, Computer Vision (ICCV), 2011 IEEE International Conference on, p.20, 2011.

N. Gelfand, Robust global registration, Third Eurographics symposium on Geometry processing, vol.38, p.197, 2005.

N. Gelfand, . Ikemoto, M. Rusinkiewicz, and . Levoy, Geometrically stable sampling for the icp algorithm, 3DIM 2003 Proceedings, 0 :260267, vol.16, p.17, 2003.

C. Glennie, Rigorous 3d error analysis of kinematic scanning lidar systems, Journal of Applied Geodesy jag, vol.1, issue.3, p.35, 2007.

Z. Gmbh, D. Fröhlich, and . Wangen, Terrestrial laser scanning new perspectives in 3d surveying, Archives, vol.36, p.713, 2004.

S. Granger and X. Pennec, Multi-scale em-icp : A fast and robust approach for surface registration, Computer VisionECCV, p.19, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00615911

D. Grant, J. Bethel, and M. Crawford, Point-to-plane registration of terrestrial laser scans, ISPRS Journal of Photogrammetry and Remote Sensing, vol.72, p.14, 2012.

A. Gressin, C. Cannelle, J. Mallet, and . Papelard, Trajectory-based registration of 3d lidar point clouds acquired with a mobile mapping system, ISPRS, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02384562

A. Gressin, . Mallet, N. Demantké, and . David, Towards 3d lidar point cloud registration improvement using optimal neighborhood knowledge, IJPRS, vol.79, p.57, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00920922

L. Tania and . Grussenmeyer, Les principes fondamentaux de la lasergrammétrie terrestre : systèmes et caractéristiques, p.34, 2011.

N. Haala, M. Peter, J. Kremer, and G. Hunter, Mobile lidar mapping for 3d point cloud collection in urban areas, a performance test. The international archives of the photogrammetry, remote sensing and spatial information sciences, vol.37, p.11191127, 2008.

D. Hahnel, W. Thrun, and . Burgard, An extension of the icp algorithm for modeling nonrigid objects with mobile robots, IJCAI, vol.18, issue.1, p.17, 2003.

J. Han, C. Chen, and C. Lo, Time-variant registration of point clouds acquired by a mobile mapping system, IEEE Geoscience and Remote Sensing Letters, vol.11, issue.1, p.14, 2014.

M. Hebel and . Stilla, Automatic registration of laser point clouds of urban areas, vol.36, p.1318, 2007.

M. Hebel and . Stilla, Automatic registration of laser point clouds of urban areas, 2007.

Y. Hecker and . Bolle, On geometric hashing and the generalized hough transform, SMC, vol.24, issue.9, p.19, 1994.

J. Ho and . Peter, An algebraic approach to ane registration of point sets minghsuan yang university of california. Computer Vision, IEEE 12th International Conference on, p.13351340, 2009.

B. Horn, Closed-form solution of absolute orientation using unit quaternions, JOSA, vol.4, issue.4, p.19, 1987.

B. Horn, S. Hilden, and . Negahdaripour, Closed-form solution of absolute orientation using orthonormal matrices, JOSA, vol.5, issue.7, p.1127, 1988.

Q. Huang, . Flöry, M. Gelfand, H. Hofer, and . Pottmann, Reassembling fractured objects by geometric matching, vol.25, p.18, 2006.

Q. Huang, M. Adams, L. Wicke, and . Guibas, Non-rigid registration under isometric deformations, Computer Graphics Forum Proc SGP, vol.27, issue.5, p.14491457, 2008.

D. Huber and M. Hebert, Fully automatic registration of multiple 3d data sets, IVC, vol.21, issue.7, p.637650, 2003.

M. Jenkinson, . Bannister, S. Brady, and . Smith, Improved optimisation for the robust and accurate linear registration and motion correction of brain images, NeuroImage, vol.17, issue.2, p.825841, 2002.

B. Jian and . Vemuri, A robust algorithm for point set registration using mixture of gaussians, vol.ICCV, p.19, 2005.

A. Johnson and . Hebert, Surface registration by matching oriented points, vol.3, p.121, 1997.

A. Johnson and . Hebert, Using spin images for ecient object recognition in cluttered 3d scenes, vol.21, p.433449, 1999.

R. Kaestner, M. Thrun, M. Montemerlo, and . Whalley, A Non-Rigid Approach to Scan Alignment and Change Detection Using Range Sensor Data, p.179194, 2006.

L. Kovar and M. Gleicher, Flexible automatic motion blending with registration curves, Work, p.214224, 2003.

S. Krishnan, . Lee, S. Moore, and . Venkatasubramanian, Global registration of multiple 3d point sets via optimization-on-a-manifold, Proceedings of the third Eurographics symposium on Geometry processing, p.187, 2005.

Y. Lamdan and H. Wolfson, Geometric hashing : A general and ecient model-based recognition scheme, Proceedings Second International Conference on Computer Vision, vol.4, p.238249, 1988.

R. L. Scouarnec, T. Touzé, J. Lacambre, and N. Seube, A new reliable boresight calibration method for mobile laser scanning applications. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.40, issue.3, p.35, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01090046

M. Leslar, J. G. Hu, and . Wang, Error analysis of a mobile terrestrial lidar system, GEOMATICA, vol.68, issue.3, p.57, 2014.

A. Levin and R. Szeliski, Visual odometry and map correlation, Proceedings of the 2004 IEEE Computer Society Conference on, vol.1, p.19, 2004.

H. Li, M. Sumner, and . Pauly, Global correspondence optimization for non-rigid registration of depth scans, Computer Graphics Forum, vol.27, issue.5, p.14211430, 2008.

H. Li and R. Hartley, The 3d-3d registration problem revisited, Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, p.20, 2007.

Y. Li and Y. Wang, An accurate registration method based on point clouds and redundancy elimination of lidar data, p.605610, 2008.

L. Liu and . Stamos, Automatic 3d to 2d registration for the photorealistic rendering of urban scenes, 2005.

Y. Liu, Automatic registration of overlapping 3d point clouds using closest points, Image and Vision Computing, vol.24, issue.7, p.762781, 2006.

P. Lothe, S. Bourgeois, F. Dekeyser, E. Royer, and M. Dhome, Bases d'amers visuels à grande échelle : correction et géolocalisation de reconstructions slam monoculaires à l'aide de modèles 3d grossiers de villes, 2009.

D. Lowe, Three-dimensional object recognition from single two-dimensional images, Articial Intelligence, vol.31, issue.3, p.355395, 1987.

K. Lyngbaek and . Zakhor, Automatic registration of aerial imagery with untextured 3d lidar models, IEEE Conference on Computer Vision and Pattern Recognition, p.18, 2008.

F. Maes, . Collignon, G. Vandermeulen, P. Marchal, and . Suetens, Multimodality image registration by maximization of mutual information, IEEE Transactions on Medical Imaging, vol.16, issue.2, p.187198, 1997.

M. Magnusson, T. Lilienthals, and . Duckett, Scan registration for autonomous mining vehicles using 3d-ndt, Journal of Field Robotics, vol.24, issue.10, p.803827, 2007.

M. Mahmoudi and G. Sapiro, Three-dimensional point cloud recognition via distributions of geometric distances, Graphical Models, vol.71, issue.1, p.2231, 2009.

J. , A. Maintz, and M. Viergever, An overview of medical image registration methods, Nature, vol.12, issue.6, p.122, 1996.

A. Makadia, K. Patterson, and . Daniilidis, Fully automatic registration of 3d point clouds, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.1, p.19, 2006.

X. Mateo, X. Orriols, and X. Binefa, Bayesian perspective for the registration of multiple 3d views, Computer Vision and Image Understanding, vol.118, p.19, 2014.

G. C-r-maurer, B. B-aboutanos, R. Dawant, J. Maciunas, and . Fitzpatrick, Registration of 3-d images using weighted geometrical features, IEEE Transactions on Medical Imaging, vol.15, issue.6, p.836849, 1996.

N. Mellado, D. Aiger, and N. Mitra, Super 4pcs fast global pointcloud registration via smart indexing, Computer Graphics Forum, vol.33, p.19, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01538738

M. Mezian, B. Vallet, B. Soheilian, and N. Paparoditis, Uncertainty propagation for terrestrial mobile laser scanner, p.55, 2016.

M. Edward, J. S. Mikhail, J. C. Bethel, and . Mcglone, American Society of Photogrammetry, Remote Sensing, Imaging, and Geospatial Information Society. Manual of photogrammetry, American Society of Photogrammetry and Remote Sensing, vol.5, p.61, 2004.

M. Miled, B. Soheilian, E. Habets, and B. Vallet, Hybrid online mobile laser scanner calibration through image alignment by mutual information. IS-PRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, vol.3, issue.1, 2016.

M. Miled, B. Soheilian, E. Habets, and B. Vallet, Hybrid online mobile laser scanner calibration through image alignment by mutual information. IS-PRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, vol.3, issue.1, p.15

N. Mitra, H. Gelfand, L. Pottmann, and . Guibas, Registration of point cloud data from a geometric optimization perspective, Proceedings of the 2004 EurographicsACM SIG-GRAPH symposium on Geometry processing SGP 04, vol.12, p.22, 2004.

J. Niloy, S. Mitra, M. Flory, N. Ovsjanikov, L. Gelfand et al., Dynamic geometry registration, Symposium on Geometry Processing, p.173182, 2007.

F. Monnier, Amélioration de la localisation 3D de véhicules mobiles à l'aide de cartes ou modèles 3D, vol.63, p.113, 2014.

. E-mouragnon, M. Lhuillier, . Dhome, P. Dekeyser, and . Sayd, Real time localization and 3d reconstruction, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.06, p.20, 2006.

P. Musialski, Point cloud to model registration, p.34, 2009.

A. Myronenko and . Song, Point set registration : Coherent point drift, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.12, p.19, 2010.

A. Myronenko and M. Song, Non-rigid point set registration : Coherent Point Drift, vol.19, p.10091016, 2007.

A. Nüchter, K. Lingemann, and J. Hertzberg, Schloss Birlinghoven, and D Sankt Augustin. 6d slam 3d mapping outdoor environments, Journal of Field Robotics, vol.24, issue.8-9, p.699722, 2007.

K. Nishino and K. Ikeuchi, Robust simultaneous registration of multiple range images, p.454461, 2002.

A. Nüchter and K. Lingemann, Joachim Hertzberg, and Hartmut Surmann. 6d slam3d mapping outdoor environments, Journal of Field Robotics, vol.24, issue.8-9, p.18, 2007.

M. Olsen, Guidelines for the use of mobile LIDAR in transportation applications, Transportation Research Board, vol.748, 2013.

C. Olsson, F. Kahl, and M. Oskarsson, The registration problem revisited : Optimal solutions from points, lines and planes, Computer Vision and Pattern Recognition, vol.1, p.20, 2006.

N. Paparoditis, J. Papelard, B. Cannelle, . Devaux, . Soheilian et al., Stereopolis II : A multi-purpose and multi-sensor 3d mobile mapping system for street visualisation and 3d metrology, RFPT, issue.6, p.6979, 2012.

N. Paparoditis, J. Papelard, B. Cannelle, A. Devaux, B. Soheilian et al., Stereopolis ii : A multi-purpose and multi-sensor 3d mobile mapping system for street visualisation and 3d metrology. Revue française de photogrammétrie et de télédétection, vol.200, p.6979, 2012.

C. Papazov and . Burschka, Deformable 3d shape registration based on local similarity transforms. Processing, vol.30, 2011.

C. Papazov and . Burschka, Stochastic global optimization for robust point set registration. Computer Vision and Image Understanding, vol.115, p.15981609, 2011.

C. Papazov and . Burschka, Stochastic optimization for rigid point set registration, Proceedings of the 5th International Symposium on Advances in Visual Computing : Part I, ISVC '09, p.10431054, 2009.

K. Pathak and D. Borrmann, Evaluation of the robustness of planar-patches based 3d-registration using marker-based ground-truth in an outdoor urban scenario, telligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, p.14, 2010.

M. Poreba, Edge-based accuracy assessment and improvement of mobile laser scanning systems. Theses, Ecole Nationale Supérieure des Mines de Paris, vol.30, p.112, 2014.
URL : https://hal.archives-ouvertes.fr/pastel-01068828

H. Pottmann, M. Leopoldseder, and . Hofer, Simultaneous registration of multiple views of a 3d object, INTERNATIONAL ARCHIVES OF PHOTOGRAMMETRY REMOTE SENSING AND SPATIAL INFORMATION SCIENCES, vol.34, issue.3, p.265270, 2002.

H. Pottmann, M. Leopoldseder, and . Hofer, Registration without icp. CVIU, vol.95, issue.1, p.20, 2004.

H. Pottmann, Y. Huang, S. Yang, and . Hu, Geometry and convergence analysis of algorithms for registration of 3d shapes, International Journal of Computer Vision, vol.67, issue.3, p.277296, 2006.

K. Pulli, Multiview registration for large data sets, Second International Conference on 3D Digital Imaging and Modeling Cat NoPR00062, vol.1, p.160168, 1999.

. T-pylvanainen, . Roimela, . Vedantham, R. Itaranta, and . Grzeszczuk, Automatic alignment and multi-view segmentation of street view data using 3d shape prior, Fifth International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), 2010.

P. Rieger, M. Studnicka, G. Pfennigbauer, and . Zach, Boresight alignment method for mobile laser scanning systems, Journal of Applied Geodesy, vol.4, issue.1, p.35, 2010.

N. Ripperda and C. Brenner, Marker free registration of terrestrial laser scans using the normal distribution transform, IAPRS, XXXVI-5/W17 (on CD-ROM, p.18, 2005.

M. Rodrigues, R. Fisher, and Y. Liu, Special issue on registration and fusion of range images, Computer Vision and Image Understanding, vol.87, issue.1-3, p.20, 2002.

K. Rohr, H. Stiehl, R. Sprengel, T. Buzug, J. Weese et al., Landmarkbased elastic registration using approximating thin-plate splines, IEEE Transactions on Medical Imaging, vol.20, issue.6, p.20, 2001.

D. Rueckert, C. Sonoda, D. Hayes, M. Hill, D. Leach et al., Nonrigid registration using free-form deformations : application to breast mr images, IEEE Transactions on Medical Imaging, vol.18, issue.8, p.20, 1999.

S. Rusinkiewicz and M. Levoy, Ecient variants of the icp algorithm, vol.3, p.49, 2001.

N. Radu-bogdan-rusu, M. Blodow, and . Beetz, Fast point feature histograms (fpfh) for 3d registration, Robotics and Automation, 2009. ICRA'09. IEEE International Conference on, p.20, 2009.

J. Salvi, . Matabosch, J. Fo, and . Forest, A review of recent range image registration methods with accuracy evaluation, IVC, vol.25, issue.5, p.578596, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00578333

J. Salvi, C. Matabosch, D. Fo, and J. Forest, A review of recent range image registration methods with accuracy evaluation, Image and Vision computing, vol.25, issue.5, p.14, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00578333

R. Sandhu, A. Dambreville, and . Tannenbaum, Point set registration via particle ltering and stochastic dynamics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.8, p.14591473, 2010.

P. Schaer, J. Skaloud, K. Landtwing, and . Legat, Accuracy estimation for laser point cloud including scanning geometry, Mobile Mapping Symposium, 2007.

J. Schnabel, M. Rueckert, J. Quist, . Blackall, . Castellano-smith et al., A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations, MICCAI, issue.8, p.573581, 2001.

J. Schnabel, M. Rueckert, J. Quist, . Blackall, . Castellano-smith et al., Tests de normalité -techniques empiriques et tests statistiques, p.117, 2011.

D. Schneider and P. Eisert, Fast nonrigid mesh registration with a data-driven deformation prior, IEEE 12th International Conference on Computer Vision Workshops ICCV Workshops, vol.141, p.304311, 2009.

A. Segal, S. Haehnel, and . Thrun, Generalized-icp, Proceedings of Robotics : Science and Systems, p.17, 2009.

L. Shang, J. Cheng-lv, and Z. Yi, Rigid medical image registration using pca neural network, Neurocomputing, vol.69, issue.13, p.19, 2006.

S. Samuel, M. B. Shapiro, and . Wilk, An analysis of variance test for normality (complete samples), Biometrika, vol.52, issue.3, p.117, 1965.

G. Sharp, D. Lee, and . Wehe, Icp registration using invariant features, PAMI, vol.24, issue.1, p.90102, 2002.

N. Snavely, R. Seitz, and . Szeliski, Modeling the world from internet photo collections, International Journal of Computer Vision, vol.80, issue.2, p.189210, 2007.

. Ss-soudarissanane, B. Lindenbergh, and . Gorte, Reducing the error in terrestrial laser scanning by optimizing the measurement set-up, International Society for Photogrammetry and Remote Sensing, p.34, 2008.

S. Soudarissanane, R. Lindenbergh, M. Menenti, and P. Teunissen, Scanning geometry : Inuencing factor on the quality of terrestrial laser scanning points, ISPRS Journal of Photogrammetry and Remote Sensing, vol.66, issue.4, p.34, 2011.

R. Staiger, The geometrical quality of terrestrial laser scanner (tls), p.34, 2005.

I. Stamos and M. Leordeanu, Automated feature-based range registration of urban scenes of large scale, 2003.

F. Stein and G. Medioni, Structural indexing : Ecient 3-d object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, p.125145, 1992.

T. Stoyanov, Maximum likelihood point cloud acquisition from a mobile platform, ICAR 2009. International Conference on, p.19, 2009.

H. Surmann, J. Nüchter, and . Hertzberg, An autonomous mobile robot with a 3d laser range nder for 3d exploration and digitalization of indoor environments, 2003.

A. Swart, J. Broere, R. Veltkamp, and R. Tan, Rened non-rigid registration of a panoramic image sequence to a lidar point cloud, Photogrammetric Image Analysis, p.14, 2011.

R. Szeliski, Matching 3-d anatomical surfaces with non-rigid deformations using octreesplines, International Journal of Computer Vision, vol.18, p.171186, 1996.

J. Tarel and N. Boujemaa, Une approche oue du recalage 3d : généricité et robustesse. Rapport de recherche n? 2716, vol.18, p.19, 1995.

P. W. Theiler and K. Schindler, Automatic registration of terrestrial laser scanner point clouds using natural planar surfaces. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, I-3 :173178, 2012.

J. D. Pascal-w-theiler, K. Wegner, and . Schindler, Markerless point cloud registration with keypoint-based 4-points congruent sets. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.2, p.19, 2013.

J. Thirion, New feature points based on geometric invariants for 3d image registration, IJCV, vol.18, issue.2, p.121137, 1996.
URL : https://hal.archives-ouvertes.fr/inria-00077266

J. Thirion, Image matching as a diusion process : an analogy with Maxwell's demons, Medical Image Analysis, vol.2, issue.3, p.243260, 1998.

E. Trucco, V. Fusiello, and . Roberto, Robust motion and correspondence of noisy 3-d point sets with missing data, 1999.

Y. Tsin and . Kanade, A correlation-based approach to robust point set registration, vol.18, p.19, 2004.

B. Vallet, Analyse et reconstruction de scènes urbaines, p.15, 2016.

P. Vanicek, J. Edward, and . Krakiwsky, Geodesy : the concepts, p.37, 2015.

W. Michael, L. Walker, R. Shao, and . Volz, Estimating 3-d location parameters using dual number quaternions, CVGIP : image understanding, vol.54, issue.3, p.358367, 1991.

F. Wang, . Vemuri, S. Rangarajan, and . Eisenschenk, Simultaneous nonrigid registration of multiple point sets and atlas construction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.11, p.19, 2008.

S. Weik, Registration of 3-d partial surface models using luminance and depth information, 3D Digital Imaging and Modeling, International Conference on, p.93, 1997.

M. Weinmann, A. Dittrich, S. Hinz, and B. Jutzi, Automatic feature-based point cloud registration for a moving sensor platform, 2013.

. W-m-wells, . Viola, . Atsumi, R. Nakajima, and . Kikinis, Multi-modal volume registration by maximization of mutual information, Medical Image Analysis, vol.1, issue.1, p.3551, 1996.

C. Witzgall and G. Cheok, Experiences with point cloud registration, NIST SPECIAL PUBLICATION SP, p.349356, 2003.

H. Wolfson and I. Rigoutsos, Geometric hashing : an overview, IEEE Computational Science and Engineering, vol.4, issue.4, p.19, 1997.

P. Xavier, J. Nicholas, and . Thirion, Landmark-based registration using features identied through dierential geometry, 2000.

P. Yan and . Bowyer, Automatic Identication Advanced Technologies IEEE Workshop on, vol.0, p.17, 2007.

Y. Zeng, C. Wang, Y. Wang, and X. Gu, Dimitris Samaras, and Nikos Paragios. Dense non-rigid surface registration using high-order graph matching, Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, p.19, 2010.

J. Zhang, S. H. Ge, C. Ong, S. Chui, C. Teoh et al., Rapid surface registration of 3d volumes using a neural network approach, Image and Vision Computing, vol.26, issue.2, p.19, 2008.

Z. Zhang, Recalage de deux nuages de points 3d, Traitement du Signal, vol.10, issue.4, p.263281, 1993.

Z. Zhang, Iterative point matching for registration of free-form curves and surfaces, IJCV, vol.13, issue.2, p.49, 1994.

H. Zhao and R. Shibasaki, A vehicle-borne urban 3-d acquisition system using singlerow laser range scanners, IEEE transactions on systems man and cybernetics Part B Cybernetics a publication of the IEEE Systems Man and Cybernetics Society, vol.33, p.658666, 2003.

Q. Zheng, . Sharf, . Tagliasacchi, H. Chen, . Zhang et al., Consensus skeleton for non-rigid space-time registration, Computer Graphics Forum, vol.29, issue.2, p.635644, 2010.

B. Zitova and . Zitova, Image registration methods : a survey. Image and Vision Computing, vol.21, p.9771000, 2003.