B. Li, 3d fully convolutional network for vehicle detection in point cloud, Intelligent Robots and Systems, 2017.

D. Zhao, Y. Chen, and L. Lv, Deep reinforcement learning with visual attention for vehicle classification, IEEE Transactions on Cognitive and Developmental Systems, vol.9, pp.356-367, 2017.

A. Geiger, P. Lenz, and R. Urtasun, Are we ready for autonomous driving? the kitti vision benchmark suite, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012.

F. Altché and A. Fortelle, An LSTM network for highway trajectory prediction, IEEE 20th International Conference on, 2017.

S. Wirges, Object detection and classification in occupancy grid maps using deep convolutional networks, 21st International Conference on Intelligent Transportation Systems (ITSC), 2018.

M. Jaritz, End-to-end race driving with deep reinforcement learning, IEEE International Conference on Robotics and Automation (ICRA), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01848067

G. Devineau, Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning, 21st International Conference on Intelligent Transportation Systems (ITSC), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01909081

D. Scaramuzza and F. Fraundorfer, Visual odometry, IEEE robotics & automation magazine, vol.18, pp.80-92, 2011.

R. Mur-artal, J. Montiel, and J. D. Tardos, ORB-SLAM: a versatile and accurate monocular SLAM system, IEEE Transactions on Robotics, vol.31, pp.1147-1163, 2015.

C. Forster, M. Pizzoli, and D. Scaramuzza, SVO: Fast semi-direct monocular visual odometry, IEEE, 2014.

A. Kendall, M. Grimes, and R. Cipolla, Posenet: A convolutional network for real-time 6-dof camera relocalization, Proceedings of the IEEE international conference on computer vision, 2015.

S. Wang, Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks, IEEE, 2017.

R. Li, Undeepvo: Monocular visual odometry through unsupervised deep learning, IEEE International Conference on Robotics and Automation (ICRA), 2018.

P. J. Besl, . Mckay, and D. Neil, Method for registration of 3-D shapes, Sensor Fusion IV: Control Paradigms and Data Structures. International Society for Optics and Photonics, pp.586-607, 1992.

J. Zhang and S. Singh, LOAM: Lidar Odometry and Mapping in Real-time, Robotics: Science and Systems, vol.2, 2014.

A. Nicolai, Deep learning for laser based odometry estimation, RSS workshop Limits and Potentials of Deep Learning in Robotics, 2016.

J. Li, Deep learning for 2D scan matching and loop closure, Intelligent Robots and Systems, 2017.

M. Velas, CNN for IMU assisted odometry estimation using velodyne LiDAR, IEEE, 2018.

M. Valente, C. Joly, and A. De-la-fortelle, An LSTM Network for Real-time Odometry Estimation, 2019.

M. Valente, C. Joly, A. De, and L. Fortelle, Fusing Laser Scanner and Stereo Camera in Evidential Grid Maps, 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01980643

J. Gleichauf, C. Pfitzner, and S. May, Sensor Fusion of a 2D Laser Scanner and a Thermal Camera, 2017.

D. Xu, D. Anguelov, and A. Jain, Pointfusion: Deep sensor fusion for 3d bounding box estimation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018.

N. Patel, Sensor modality fusion with CNNs for UGV autonomous driving in indoor environments, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.

, IEEE, 2017.

G. Grisetti, A tutorial on graph-based SLAM, IEEE Intelligent Transportation Systems Magazine, vol.2, pp.31-43, 2010.

L. Li and H. Lin, Ordinal regression by extended binary classification, 2007.

Z. Niu, Ordinal regression with multiple output cnn for age estimation, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation, vol.9, pp.1735-1780, 1997.

R. Pascanu, T. Mikolov, and Y. Bengio, On the difficulty of training recurrent neural networks, International Conference on Machine Learning, 2013.

A. Dosovitskiy, P. Fischery, E. Ilg, C. Hazirbas, V. Golkov et al., Flownet: Learning optical flow with convolutional networks, Proceedings of IEEE InternationalConference on Computer Vision (ICCV), pp.2758-2766, 2015.