J. Zogg, GPS: Essentials of Satellite Navigation: Compendium: Theorie and Principles of Satellite Navigation, Overview of GPS/GNSS Systems and Applications, 2009.

. Edmap, Enhanced Digital Mapping Project Final Report, 2004.

S. Thrun, D. Fox, W. Burgard, and F. Dellaert, Robust Monte Carlo Localization for Mobile Robots, Artif. Intell. J, vol.101, pp.99-141, 2001.

D. Nistér, O. Naroditsky, and J. Bergen, Visual odometry, Proc, vol.1, pp.652-659, 2004.

J. Zhang and S. Singh, LOAM : Lidar Odometry and Mapping in Real-time, Robot. Sci. Syst, 2014.

H. Durrant-whyte and T. Bailey, Simultaneous localization and mapping: Part I, IEEE Robot. Autom. Mag, vol.13, issue.2, pp.99-108, 2006.

T. Bailey and H. Durrant-whyte, Simultaneous localization and mapping (SLAM): Part II, IEEE Robot. Autom. Mag, vol.13, issue.3, pp.108-117, 2006.

J. Fuentes-pacheco, J. Ruiz-ascencio, and J. M. Rendón-mancha, Visual simultaneous localization and mapping: a survey, Artif. Intell. Rev, vol.43, issue.1, pp.55-81, 2012.

F. Moosmann and C. Stiller, Velodyne SLAM, IEEE Intell. Veh. Symp. Proc. IEEE, pp.393-398, 2011.

A. Nuchter, K. Lingemann, and J. Hertzberg, 6D SLAM-3D Mapping Outdoor Environments, J. F. Robot, vol.24, pp.699-722, 2007.

M. Buczko and V. Willert, Efficient Global Localization Using Vision and Digital Offline Map, pp.1689-1694, 2017.

Z. Zhu, T. Oskiper, S. Samarasekera, R. Kumar, and H. S. Sawhney, Real-time global localization with a pre-built visual landmark database, 26th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR, 2008.

F. Chausse, J. Laneurit, and R. Chapuis, Vehicle localization on a digital map using particles filtering, IEEE Intell. Veh. Symp. Proc, pp.243-248, 2005.

J. Ko?ecká, F. Li, and X. Yang, Global localization and relative positioning based on scale-invariant keypoints, Rob. Auton. Syst, vol.52, issue.1, pp.27-38, 2005.

T. Wu and A. Ranganathan, Vehicle localization using road markings, IEEE Intell. Veh. Symp. Proc. IEEE, pp.1185-1190, 2013.
DOI : 10.1109/ivs.2013.6629627

H. Li, F. Nashashibi, and G. Toulminet, Localization for intelligent vehicle by fusing mono-camera, low-cost GPS and map data, IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC. IEEE, pp.1657-1662, 2010.
DOI : 10.1109/itsc.2010.5625240

URL : https://hal.archives-ouvertes.fr/hal-00646666

J. Levinson and S. Thrun, Robust vehicle localization in urban environments using probabilistic maps, Robot. Autom. (ICRA), pp.4372-4378, 2010.
DOI : 10.1109/robot.2010.5509700

URL : http://ais.informatik.uni-freiburg.de/teaching/ws10/robotnav_seminar/levinson10icra.pdf

H. Fu, L. Ye, R. Yu, and T. Wu, An efficient scan-to-map matching approach for autonomous driving, 2016 IEEE Int. Conf. Mechatronics Autom. IEEE ICMA, pp.1649-1654, 2016.
DOI : 10.1109/icma.2016.7558811

E. Pollard, J. Perez, and F. Nashashibi, Step and curb detection for autonomous vehicles with an algebraic derivative-based approach applied on laser rangefinder data, IEEE Intell. Veh. Symp. Proc, pp.684-689, 2013.
DOI : 10.1109/ivs.2013.6629546

URL : https://hal.archives-ouvertes.fr/hal-00841669

Z. J. Chong, B. Qin, T. Bandyopadhyay, M. H. Ang, E. Frazzoli et al., Mapping with synthetic 2D LIDAR in 3D urban environment, IEEE Int. Conf. Intell. Robot. Syst, pp.4715-4720, 2013.
DOI : 10.1109/iros.2013.6697035

A. Hata and D. Wolf, Road marking detection using LIDAR reflective intensity data and its application to vehicle localization, 17th Int. IEEE Conf. Intell. Transp. Syst, pp.584-589, 2014.
DOI : 10.1109/itsc.2014.6957753

S. Kammel and B. Pitzer, Lidar-based lane marker detection and mapping, IEEE Intell. Veh. Symp. Proc, pp.1137-1142, 2008.
DOI : 10.1109/ivs.2008.4621318

B. He, R. Ai, Y. Yan, and X. Lang, Lane marking detection based on convolution neural network from point clouds, 2016 IEEE 19th Int. Conf. Intell. Transp. Syst. IEEE, pp.2475-2480, 2016.

W. Zhang, LIDAR-based road and road-edge detection, IEEE Intell. Veh. Symp. Proc. IEEE, pp.845-848, 2010.
DOI : 10.1109/ivs.2010.5548134

A. Y. Hata, F. S. Osorio, and D. F. Wolf, Robust Curb Detection and Vehicle Localization in Urban Environments
DOI : 10.1109/ivs.2014.6856405

M. Montemerlo, J. Becker, S. Bhat, H. Dahlkamp, D. Dolgov et al., Junior: The stanford entry in the urban challenge, Springer Tracts Adv. Robot, vol.56, pp.91-123, 2009.
DOI : 10.1007/978-3-642-03991-1_3

URL : http://www-cs.stanford.edu/people/petrovsk/dn/publications/montemerlo_fsr08.pdf

R. O. Duda and P. E. Hart, Use of the Hough transformation to detect lines and curves in pictures, Commun. ACM, vol.15, issue.1, pp.11-15, 1972.

E. Héry, S. Masi, P. Xu, and P. Bonnifait, Map-based Curvilinear Coordinates for Autonomous Vehicles, pp.1699-1705, 2017.

R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Series D, vol.82, pp.35-45, 1960.
DOI : 10.1115/1.3662552

URL : http://fluidsengineering.asmedigitalcollection.asme.org/data/journals/jfega4/27220/35_1.pdf

F. Dellaert, D. Fox, W. Burgard, and S. Thrun, Monte carlo localization for mobile robots, Robot. Autom, 1999.
DOI : 10.1109/robot.1999.772544

URL : http://www.informatik.uni-freiburg.de/~burgard/abstracts/../postscripts/robustMonteCarlo.pdf

R. Schubert, C. Adam, M. Obst, N. Mattern, V. Leonhardt et al., Empirical evaluation of vehicular models for ego motion estimation, IEEE Intell. Veh. Symp. Proc, issue.Iv, pp.534-539, 2011.
DOI : 10.1109/ivs.2011.5940526

O. C. Douc, Comparison of resampling schemes for particle filtering, ISPA 2005. Proc. 4th Int. Symp. Image Signal Process. Anal, pp.64-69, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00005883