S. Ochmann, R. Vock, R. Wessel, R. Et, and . Klein, Automatic reconstruction of parametric building models from indoor point clouds, Computers and Graphics (Pergamon), vol.54, pp.94-103, 2016.

C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza et al., Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age, IEEE Transactions on Robotics, vol.32, issue.6, pp.1309-1332, 2016.

P. Besl and N. Mckay, A Method for Registration of 3-D Shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, pp.239-256, 1992.

P. Brou, Using the Gaussian Image to Find the Orientation of Objects, The International Journal of Robotics Research, vol.3, issue.4, pp.89-125, 1984.

S. Bum-kang and K. Ikeuchi, Determining 3-D object pose using the complex extended Gaussian image, Computer Vision and Pattern Recognition (CVPR), pp.580-585, 1991.

F. Pomerleau, F. Colas, and R. Et, Siegwart. A Review of Point Cloud Registration Algorithms for Mobile Robotics, Foundations and Trends in Robotics, vol.4, issue.1, pp.1-104, 2015.

Z. Zhang, Iterative point matching for registration of free-form curves and surfaces, International Journal of Computer Vision, vol.13, issue.2, pp.119-152, 1994.

A. Segal, . Haehnel, and . Thrun, Generalized-ICP. Robotics: Science and Systems, vol.5, pp.168-176, 2009.

. Andrew-w-fitzgibbon, Robust registration of 2d and 3d point sets, vol.21, pp.1145-1153, 2002.

J. Yang, H. Li, Y. Et, and . Jia, Go-ICP: Solving 3D registration efficiently and globally optimally, International Conference on Computer Vision (ICCV), pp.1457-1464, 2013.

D. Holz, A. E. Ichim, F. Tombari, R. B. Rusu, and E. S. Behnke, Registration with the point cloud library: A modular framework for aligning in 3-d, IEEE Robotics Automation Magazine, vol.22, issue.4, p.2015

S. Filipe, A. Luís, and . Alexandre, A comparative evaluation of 3d keypoint detectors in a rgb-d object dataset, International Conference on Computer Vision Theory and Applications (VISAPP), vol.1, pp.476-483, 2014.

N. Radu-bogdan-rusu, . Blodow, M. Et, and . Beetz, Fast Point Feature Histograms (FPFH) for 3D registration, IEEE International Conference on Robotics and Automation, pp.3212-3217, 2009.

A. E. Johnson and . Hebert, Using spin images for efficient object recognition in cluttered 3D scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol.21, pp.433-449, 1999.

F. Tombari, S. Salti, L. D. Et, and . Stefano, Unique signatures of histograms for local surface description, European Conference on Computer Vision (ECCV), pp.356-369, 2010.

Q. Zhou, J. Park, and . Et-vladlen-koltun, Fast Global Registration, pp.766-782, 2016.

A. Martin, . Fischler, C. Robert, and . Bolles, Paradigm for Model, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981.

N. Mellado, D. Aiger, J. Et-niloy, and . Mitra, Super 4PCS fast global pointcloud registration via smart indexing, Computer Graphics Forum, vol.33, issue.5, pp.205-215, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01538738

K. Pathak and A. Birk, Fast Registration Based on Noisy Planes with Unknown Correspondences for 3D Mapping, IEEE Transactions on Robotics, vol.26, issue.3, pp.424-441, 2010.

M. Magnusson, The Three-Dimensional NormalDistributions Transform -an Efficient Representation for Registration, Surface Analysis, and Loop Detection, 2009.

C. Yizong, Mean shift, mode seeking, and clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol.17, pp.790-799, 1995.

F. Pomerleau, M. Liu, F. Colas, R. Et, and . Siegwart, Challenging data sets for point cloud registration algorithms, The International Journal of Robotics Research, vol.31, issue.14, p.2012
URL : https://hal.archives-ouvertes.fr/hal-01143454