A. Aguilar-gonzález, M. Arias-estrada, and F. Berry, Monocular-SLAM: A survey, International Journal of Advanced Robotic Systems (IJARS, 2019.

A. Aguilar-gonzález, M. Arias-estrada, F. Berry, J. A. Osuna-coutiño, and . De-jesús, The Fastest Visual Ego-motion Algorithm in the West, Microprocessors and Microsystems, vol.67, pp.103-116, 2019.

A. Aguilar-gonzález, M. Arias-estrada, and F. Berry, Depth from motion algorithm and hardware architecture for smart cameras. Sensors -Special Issue "Depth Sensors and 3D Vision, 2019.

A. Aguilar-gonzález, M. Arias-estrada, and F. Berry, Robust feature extraction algorithm suitable for real-time embedded applications, Journal of Real-Time Image Processing, vol.14, issue.3, 2018.

, Conference proceedings

A. Aguilar-gonzález and M. Arias-estrada, Towards a smart camera for monocular SLAM, Proceedings of the 10th International Conference on Distributed Smart Camera, pp.128-135, 2016.

A. Aguilar-gonzález and M. Arias-estrada, Dense mapping for monocular-SLAM, Indoor Positioning and Indoor Navigation (IPIN), 2016 International Conference on, pp.1-8, 2016.

A. Aguilar-gonzález, M. Arias-estrada, and F. Berry, Dense Feature Matching Core for FPGA-based Smart Cameras, Proceedings of the 11th International Conference on Distributed Smart Cameras, pp.41-48, 2017.

J. A. Osuna-coutiño, . De-jesús, A. Aguilar-gonzález, and M. Arias-estrada, GPU-based Visual Odometry for Autonomous Vehicle Applications, Proceedings of the 11th International Conference on Distributed Smart Cameras, pp.210-211, 2017.

A. Aguilar-gonzález, M. Arias-estrada, and F. Berry, Camera Pose Estimation Suitable for Smart Cameras, Proceedings of the 11th International Conference on Distributed Smart Cameras, pp.202-204, 2017.

A. Aguilar-gonzález and M. Arias-estrada, Dense mapping for monocular-SLAM, Proceedings of the 2016 IEEE International Conference on Indoor Positioning and Indoor Navigation, pp.1-8, 2016.

A. Aguilar-gonzález and M. Arias-estrada, Towards a smart camera for monocular-SLAM, Proceedings of the 10th International Conference on Distributed Smart Cameras, ICDSC 2016, pp.128-135, 2016.

A. Aguilar-gonzález, M. Arias-estrada, M. Pérez-patricio, and J. L. Camas-anzueto, An FPGA 2D-convolution unit based on the CAPH language, Journal of Real-Time Image Processing, pp.1-15, 2015.

A. Aguilar-gonzález, M. Arias-estrada, and F. Berry, Camera pose estimation suitable for smart cameras, Proceedings of the 11th International Conference on Distributed Smart Cameras, pp.202-204, 2017.

A. Aguilar-gonzález, M. Arias-estrada, and F. Berry, Dense feature matching core for FPGA-based smart cameras, Proceedings of the 11th International Conference on Distributed Smart Cameras, pp.41-48, 2017.

A. Aguilar-gonzález, M. Arias-estrada, and F. Berry, Robust feature extraction algorithm suitable for real-time embedded applications, Journal of Real-Time Image Processing, vol.14, issue.3, pp.647-665, 2018.

A. Aguilar-gonzález, Visual Odometry Algorithm and Architecture for FPGA Acceleration, p.10, 2018.

A. Aguilar-gonzález, The Fastest Visual Ego-motion Algorithm in the West, 2018.

A. Aguilar-gonzález, INAOE/DREAM benchmark dataset, 2018.

P. F. Alcantarilla, J. J. Yebes, J. Almazán, and L. M. Bergasa, On combining visual SLAM and dense scene flow to increase the robustness of localization and mapping in dynamic environments, Proceedings of the 2012 IEEE International Conference on Robotics and Automation, ICRA 2012, pp.1-6, 2012.

T. Bailey and H. Durrant-whyte, Simultaneous localization and mapping (SLAM): Part II, IEEE Robotics & Automation Magazine, vol.13, issue.3, pp.108-117, 2006.

S. Y. Bao, M. Bagra, Y. W. Chao, and S. Savarese, Semantic structure from motion with points, regions, and objects, Proceedings of the 2012 IEEE International Conference on Computer Vision and Pattern Recognition, pp.2703-2710, 2012.

F. Barranco, M. Tomasi, J. Diaz, M. Vanegas, and E. Ros, Parallel architecture for hierarchical optical flow estimation based on FPGA, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol.20, pp.1058-1067, 2012.

J. L. Barron and N. A. Thacker, Tutorial: Computing 2D and 3D optical flow, Imaging Science and Biomedical Engineering Division, 2007.

H. Bay, A. Ess, T. Tuytelaars, and L. Van-gool, Speeded-Up Robust Features (SUFR), vol.110, pp.346-359, 2008.

J. Behley and C. Stachniss, Efficient surfel-based slam using 3D laser range data in urban environments, Proceedings of Robotics: Science and Systems (RSS), pp.1-10, 2018.

S. Benhimane and E. Malis, Homography-based 2D visual tracking and servoing, The International Journal of Robotics Research, vol.26, issue.7, pp.661-676, 2007.

P. Bergmann, R. Wang, and D. Cremers, Online photometric calibration of auto exposure video for realtime visual odometry and SLAM, IEEE Robotics and Automation Letters, vol.3, issue.2, pp.627-634, 2018.

P. J. Besl and N. D. Mckay, Method for registration of 3D shapes, Sensor Fusion IV: Control Paradigms and Data Structures, vol.1611, pp.586-607, 1992.

M. Birem and F. Berry, Dreamcam: A modular FPGA-based smart camera architecture, Journal of Systems Architecture, vol.60, issue.6, pp.519-527, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01625648

S. R. Bista, P. R. Giordano, and F. Chaumette, Appearance-based indoor navigation by using line segments, IEEE Robotics and Automation Letters, vol.1, issue.1, pp.423-430, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01259750

J. Biswas and M. Veloso, Depth camera based indoor mobile robot localization and navigation, Proceedings of the 2012 IEEE International Conference on Robotics and Automation, ICRA 2012, pp.1697-1702, 2012.

B. Li, Y. Dai, and M. He, Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference, Pattern Recognition, vol.83, pp.28-339, 2018.

G. Bourmaud and R. Megret, Robust large scale monocular visual SLAM, Proceedings of the 2015 IEEE International Conference on Computer Vision and Pattern Recognition, pp.1638-1647, 2012.

R. G. Brown and P. Y. Hwang, Introduction to random signals and applied Kalman filtering, 1992.

M. Calonder, V. Lepetit, C. Strecha, and P. Fua, BRIEF: Binary Robust Independent Elementary Features, Proceedings of the 2010 European conference on computer vision, ECCV 2010, pp.778-792, 2010.

S. Caux, E. Hendrickx, F. Berry, M. Pelcat, and J. Sérot, Demo GPStudio: a toolchain for FPGA-based smart cameras, Proceedings of the 10th International Conference on Distributed Smart Camera, ICDSC 2016, pp.778-792, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01626469

D. Chekhlov, A. P. Gee, A. Calway, and W. Mayol-cuevas, Ninja on a plane: Automatic discovery of physical planes for augmented reality using visual SLAM, Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp.1-4, 2007.

Y. Cheng, Mean shift, mode seeking, and clustering, IEEE transactions on pattern analysis and machine intelligence, vol.17, pp.790-799, 1995.

T. A. Ciarfuglia, G. Costante, P. Valigi, and E. Ricci, Evaluation of non-geometric methods for visual odometry, Robotics and Autonomous Systems, vol.62, issue.12, pp.1717-1730, 2014.

J. Civera, A. J. Davison, and J. M. Martinez-montiel, Inverse depth parametrization for monocular SLAM, IEEE transactions on robotics, vol.24, issue.5, pp.932-945, 2008.

A. Concha and J. Civera, Using superpixels in monocular-SLAM, Proceedings of the 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, pp.365-372, 2014.

A. Concha-belenguer and J. Civera-sancho, DPPTAM: Dense Piecewise Planar Tracking And Mapping from a monocular sequence, Proceedings of the 2015 IEEE International Conference on Intelligent Robots and Systems, IROS 2015, pp.1-8, 2015.

G. Costante, M. Mancini, P. Valigi, and T. A. Ciarfuglia, Exploring representation learning with cnns for frame-to-frame ego-motion estimation, IEEE robotics and automation letters, vol.1, issue.1, pp.18-25, 2016.

A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse, Monoslam: Real-time single camera SLAM, IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.6, pp.1052-1067, 2007.

O. Demetz, D. Hafner, and J. Weickert, The complete rank transform: A tool for accurate and morphologically invariant matching of structure, Proceedings of the 2013 British Machine Vision Conference, BMVC 2013, pp.1-12, 2013.

J. Díaz, E. Ros, F. Pelayo, E. M. Ortigosa, and S. Mota, FPGA-based real-time optical-flow system, IEEE transactions on circuits and systems for video technology, vol.16, pp.274-279, 2006.

Z. Dong, G. Zhang, J. Jia, and H. Bao, Efficient keyframe-based real-time camera tracking, Computer Vision and Image Understanding, vol.118, pp.97-110, 2014.

H. Durrant-whyte and T. Bailey, Simultaneous localization and mapping: part I, IEEE robotics & automation magazine, vol.13, pp.99-110, 2006.

J. Engel, J. Sturm, and D. Cremers, Semi-dense visual odometry for a monocular camera, Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 2013, pp.1449-1456, 2013.

J. Engel, T. Schöps, and D. Cremers, LSD-SLAM: Large-Scale Direct monocular-SLAM, Proceedings of the 2014 European Conference on Computer Vision, pp.834-849, 2013.

J. Engel, J. Stückler, and D. Cremers, Large-scale direct SLAM with stereo cameras, Proceedings of the 2015 IEEE International Conference on Intelligent Robots and Systems, pp.1935-1942, 2015.

J. Engel, V. Koltun, and D. Cremers, Direct sparse odometry, IEEE transactions on pattern analysis and machine intelligence, vol.40, pp.611-625, 2018.

M. Fanfani, F. Bellavia, and C. Colombo, Accurate keyframe selection and keypoint tracking for robust visual odometry, Machine Vision and Applications, vol.27, pp.833-844, 2016.

O. D. Faugeras and F. Lustman, Motion and structure from motion in a piecewise planar environment, International Journal of Pattern Recognition and Artificial Intelligence, vol.2, issue.3, pp.485-508, 1988.
URL : https://hal.archives-ouvertes.fr/inria-00075698

C. Forster, M. Pizzoli, and D. Scaramuzza, SVO: Fast Semi-direct monocular Visual Odometry, Proceedings of the 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, pp.15-22, 2014.

H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao, Deep Ordinal Regression Network for Monocular Depth Estimation, Proceedings of the 2018 IEEE International Conference on Computer Vision and Pattern Recognition, pp.1-8, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01741163

D. Gálvez-lópez, M. Salas, J. D. Tardós, and J. M. Montiel, Real-time monocular object SLAM, Robotics and Autonomous Systems, vol.75, pp.435-449, 2016.

A. Geiger, J. Ziegler, and C. Stiller, Stereoscan: Dense 3D reconstruction in realtime, Proceedings of the 2011 IEEE Intelligent Vehicles Symposium, pp.963-968, 2011.

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, Vision meets robotics: The KITTI dataset, The International Journal of Robotics Research, vol.32, pp.1231-1237, 2013.

A. Glover, W. Maddern, M. Warren, S. Reid, M. Milford et al., Openfabmap: An open source toolbox for appearance-based loop closure detection, Proceedings of the 2012 IEEE International Conference on Robotics and Automation, ICRA 2012, pp.4730-4735, 2012.

R. Gomez-ojeda and J. Gonzalez-jimenez, Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments, Proceedings of the 2016 IEEE International Conference on Robotics and Automation, vol.2016, pp.1130-1137, 2016.

J. Graeter, A. Wilczynski, and M. Lauer, LIMO: Lidar-Monocular Visual Odometry, Proceedings of the 2018 IEEE International Conference on Intelligent Robots and Systems, IROS 2018, pp.1-8, 2018.

A. Graves, S. Lim, and T. Fagan, Visual odometry using convolutional neural networks, The Kennesaw Journal of Undergraduate Research, vol.5, issue.3, pp.1-10, 2017.

W. N. Greene, K. Ok, P. Lommel, and N. Roy, Multi-level mapping: Real-time dense monocular-SLAM, Proceedings of the 2016 IEEE International Conference on Robotics and Automation, vol.2016, pp.833-840, 2016.

C. Harris and M. Stephens, A combined corner and edge detector, Proceedings of the 4th Alvey vision conference, vol.6, pp.23-24, 1988.

R. Hartley and A. Zisserman, Multiple view geometry in computer vision, 2003.

R. I. Hartley and P. Sturm, Triangulation, Computer vision and image understanding, vol.68, pp.146-157, 1997.
URL : https://hal.archives-ouvertes.fr/inria-00525713

S. Hengstler, D. Prashanth, S. Fong, and H. Aghajan, Mesheye: a hybrid-resolution smart camera mote for applications in distributed intelligent surveillance, Proceedings of the 6th International Conference on Information Processing in Sensor Networks, ICIPSN, pp.360-369, 2007.

C. D. Herrera, K. Kim, J. Kannala, K. Pulli, and J. Heikkilä, DT-SLAM: Deferred Triangulation for robust SLAM, Proceedings of the 2nd International Conference on 3D Vision, vol.3, pp.609-616, 2014.

T. Holzmann, F. Fraundorfer, and H. Bischof, Direct stereo visual odometry based on lines, Proceedings of the 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp.1-11, 2016.

D. Honegger, P. Greisen, L. Meier, P. Tanskanen, and M. Pollefeys, Real-time velocity estimation based on optical flow and disparity matching, Proceedings of the 2012 IEEE International Conference on Intelligent Robots and Systems, IROS 2012, pp.5177-5182, 2012.

B. K. Horn and B. G. Schunck, Determining optical flow, Artificial intelligence, vol.17, pp.185-203, 1981.

J. Huai, C. Toth, and D. Grejner-brzezinska, Stereo-inertial odometry using nonlinear optimization, Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation, ION GNSS+ 2015, pp.1-8, 2015.

T. W. Hui, X. Tang, and C. C. Loy, Liteflownet: A lightweight convolutional neural network for optical flow estimation, Proceedings of the 2018 IEEE International Conference on Computer Vision and Pattern Recognition, pp.1-8, 2018.

A. Jaegle, S. Phillips, and K. Daniilidis, Fast, robust, continuous monocular egomotion computation, Proceedings of the 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, pp.773-780, 2016.

K. Jo, M. Gupta, and S. K. Nayar, Spedo: 6 DOF ego-motion sensor using speckle defocus imaging, Proceedings of the 2015 IEEE International Conference on Computer Vision, pp.4319-4327, 2015.

M. Kaess, K. Ni, and F. Dellaert, Flow separation for fast and robust stereo odometry, 2009.

G. Klein and D. Murray, Parallel tracking and mapping for small ar workspaces, Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp.225-234, 2007.

G. Klein and D. Murray, Improving the agility of keyframe-based SLAM, Proceedings of the 2008 European Conference on Computer Vision, pp.802-815, 2008.

K. R. Konda and R. Memisevic, Learning visual odometry with a convolutional network, International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp.486-490, 2015.

R. Kümmerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard, g2o: A general framework for graph optimization, Proceedings of the 2011 IEEE International Conference on Robotics and Automation, vol.2011, pp.3607-3613, 2011.

A. Kundu, Y. Li, F. Dellaert, F. Li, and J. M. Rehg, Joint semantic segmentation and 3D reconstruction from monocular video, Proceedings of the 2014 European Conference on Computer Vision, pp.703-718, 2014.

S. H. Lee and J. Civera, Loosely-coupled semi-direct monocular SLAM, Proceedings of the 2018 European Conference on Computer Vision, ECCV 2018, Munich, pp.1-8, 2018.

V. Lepetit, F. Moreno-noguer, and P. Fua, Epnp: An accurate o (n) solution to the pnp problem, International Journal of Computer Vision, vol.81, issue.155, pp.185-203, 2009.

R. Li, S. Wang, Z. Long, and D. Gu, Undeepvo: Monocular visual odometry through unsupervised deep learning, Proceedings of the 2018 IEEE International Conference on Robotics and Automation, pp.7286-7291, 2018.

Y. Li and W. Chu, A new non-restoring square root algorithm and its VLSI implementations, Proceedings of the 1996 IEEE International Conference on Computer Design: VLSI in Computers and Processors, ICCD'96, pp.538-544, 1996.

H. Lim, J. Lim, and H. J. Kim, Real-time 6-DOF monocular visual SLAM in a large-scale environment, Proceedings of the 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, pp.1532-1539, 2014.

J. Lim, J. M. , F. , M. , and P. , Online environment mapping, Proceedings of the 2011 IEEE International Conference on Computer Vision and Pattern Recognition, pp.1-6, 2011.

H. Liu, G. Zhang, and H. Bao, Robust keyframe-based monocular SLAM for augmented reality, Proceedings of the 15th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp.1-10, 2016.

H. C. Longuet-higgins, A computer algorithm for reconstructing a scene from two projections, Nature, vol.293, pp.133-135, 1981.

D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the 1999 IEEE International Conference on Computer Vision, ICCV 1999, pp.1150-1157, 1999.

B. D. Lucas and T. Kanade, An iterative image registration technique with an application to stereo vision, Proceedings of the 7th International Joint Conference on Artificial Intelligence, IJCAI'81, pp.674-679, 1981.

W. Maddern and P. Newman, Real-time probabilistic fusion of sparse 3D LiDAR and dense stereo, Proceedings of the 2016 IEEE International Conference on Intelligent Robots and Systems, IROS 2016, pp.2181-2188, 2016.

R. Mahjourian, M. Wicke, and A. Angelova, Unsupervised learning of depth and ego-motion from monocular video using 3D geometric constraints, Proceedings of the 2018 International Conference on Computer Vision and Pattern Recognition, pp.5667-5675, 2018.

R. A. Maronna, D. Martin, and R. S. Yohai, Wiley series in probability and statistics, 2006.

J. L. Martín, A. Zuloaga, C. Cuadrado, J. Lázaro, and U. Bidarte, Hardware implementation of optical flow constraint equation using FPGAs, Computer Vision and Image Understanding, vol.98, issue.3, pp.462-490, 2005.

J. Martínez-carranza and A. Calway, Unifying planar and point mapping in monocular-SLAM, Proceedings of the 2010 British Machine Vision Conference, BMVC 2010, pp.1-11, 2010.

W. Meiqing, L. Siew-kei, and S. Thambipillai, A framework for fast and robust visual odometry, IEEE Transaction on Intelligent Transportation Systems, vol.18, issue.12, pp.3433-3448, 2017.

S. Meister, J. Hur, and S. Roth, Unflow: Unsupervised learning of optical flow with a bidirectional census loss, Proceedings of the 2016 British Machine Vision Conference, BMVC 2016, pp.1-11, 2016.

V. Mohanty, S. Agrawal, S. Datta, A. Ghosh, V. Sharma et al., Deepvo: a deep learning approach for monocular visual odometry, Proceedings of the 2017 British Machine Vision Conference, pp.1-11, 2017.

E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, and P. Sayd, Real time localization and 3D reconstruction, Proceedings of the 2006 International Conference on Computer Vision and Pattern Recognition, pp.363-370, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00091145

A. Mulloni, M. Ramachandran, G. Reitmayr, D. Wagner, R. Grasset et al., User friendly SLAM initialization, Proceedings of the 12th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp.153-162, 2013.

R. Mur-artal and J. D. Tardós, ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras, IEEE Transactions on Robotics, vol.33, issue.5, pp.1255-1262, 2017.

R. Mur-artal, J. M. Martinez-montiel, and J. D. Tardos, ORB-SLAM: a versatile and accurate monocular-SLAM system, IEEE Transactions on Robotics, vol.31, issue.5, pp.1147-1163, 2015.

R. Newcombe and A. J. Davison, Live dense reconstruction with a single moving camera, Proceedings of the 2010 IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp.1498-1505, 2010.

R. Newcombe, S. Lovegrove, and A. J. Davison, DTAM: Dense Tracking and Mapping in real-time, Proceedings of the 2011 IEEE International Conference on Computer Vision, pp.2320-2327, 2011.

D. Nistér, An efficient solution to the five-point relative pose problem, IEEE transactions on pattern analysis and machine intelligence, vol.26, pp.756-770, 2004.

C. F. Olson, L. H. Matthies, J. R. Wright, R. Li, and K. Di, Visual terrain mapping for mars exploration, Computer Vision and Image Understanding, vol.105, pp.73-85, 2007.

G. Pascoe, W. Maddern, M. Tanner, P. Piniés, and P. Newman, NID-SLAM: Robust monocular SLAM using Normalised Information Distance, Proceedings of the 2017 IEEE International Conference on Computer Vision and Pattern Recognition, pp.1-8, 2017.

M. Pérez-patricio, A. Aguilar-gonzález, M. Arias-estrada, H. R. Hernandez-de-leon, J. L. Camas-anzueto et al., An FPGA stereo matching unit based on fuzzy logic, Microprocessors and Microsystems, vol.42, pp.87-99, 2016.

M. Persson, T. Piccini, M. Felsberg, and R. Mester, Robust stereo visual odometry from monocular techniques, Proceedings of the 2015 IEEE Intelligent Vehicles Symposium, pp.686-691, 2015.

S. Pillai and J. Leonard, Monocular slam supported object recognition, Proceedings of the 2016 British Machine Vision Conference, BMVC 2016, pp.1-10, 2016.

S. Pillai and J. J. Leonard, Towards visual ego-motion learning in robots, Proceedings of the 2017 IEEE International Conference on Intelligent Robots and Systems, pp.1-6, 2017.

C. Pirchheim and G. Reitmayr, Homography-based planar mapping and tracking for mobile phones, Proceedings of the 10th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp.27-36, 2011.

C. Pirchheim, D. Schmalstieg, and G. Reitmayr, Handling pure camera rotation in keyframe-based SLAM, Proceedings of the 12th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp.229-238, 2013.

T. Pire, T. Fischer, G. Castro, P. De-cristóforis, J. Civera et al., S-PTAM: Stereo Parallel Tracking and Mapping, vol.93, pp.27-42, 2017.

K. Pirker, M. Rüther, and H. Bischof, Cd-SLAM: Continuous localization and mapping in a dynamic world, Proceedings of the 2015 IEEE International Conference on Intelligent Robots and Systems, IROS 2011, pp.3990-3997, 2011.

A. Plyer, G. Le-besnerais, and F. Champagnat, Massively parallel lucas kanade optical flow for real-time video processing applications, Journal of Real-Time Image Processing, vol.11, issue.4, pp.713-730, 2014.

A. Pretto, E. Menegatti, and E. Pagello, Omnidirectional dense large-scale mapping and navigation based on meaningful triangulation, Proceedings of the 2011 IEEE International Conference on Robotics and Automation, vol.2011, pp.3289-3296, 2011.

A. Pumarola, A. Vakhitov, A. Agudo, A. Sanfeliu, and F. Moreno-noguer, PL-SLAM: Real-time monocular visual SLAM with points and lines, Proceedings of the 2017 IEEE International Conference on Robotics and Automation, pp.4503-4508, 2017.

E. Rosten and T. Drummond, Fusing points and lines for high performance tracking, Proceedings of the 2005 IEEE International Conference on Computer Vision, ICCV'05, pp.1508-1515, 2005.

E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, ORB: An efficient alternative to SIFT or SURF, Proceedings of the 2011 IEEE International Conference on Computer Vision, pp.2564-2571, 2011.

S. Schubert, P. Neubert, and P. Protzel, Towards camera based navigation in 3D maps by synthesizing depth images, Proceedings of the Anual Conference Towards Autonomous Robotic Systems, pp.601-616, 2017.

T. Senst, J. Geistert, I. Keller, and T. Sikora, Robust local optical flow estimation using bilinear equations for sparse motion estimation, Proceedings of the 20th IEEE International Conference on Image Processing, pp.1-8, 2013.

J. Shi and C. Tomasi, Good features to track, 1993.

G. Silveira, E. Malis, and P. Rives, An efficient direct approach to visual SLAM, IEEE transactions on robotics, vol.24, issue.5, pp.969-979, 2008.

E. Simo-serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua et al., Discriminative learning of deep convolutional feature point descriptors, Proceedings of the 2015 IEEE International Conference on Computer Vision, pp.118-126, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02432714

R. Smith, M. Self, and P. Cheeseman, Estimating uncertain spatial relationships in robotics, Proceedings of the 1987 IEEE International Conference on Autonomous robot vehicles, pp.167-193, 1987.

S. Smith and J. M. Brady, Susan-a new approach to low level image processing, International Journal of Computer Vision, vol.23, issue.1, pp.45-78, 1997.

S. Song and M. Chandraker, Robust scale estimation in real-time monocular SfM for autonomous driving, Proceedings of the 2014 IEEE International Conference on Computer Vision and Pattern Recognition, pp.24-27, 2014.

S. Song, M. Chandraker, and C. C. Guest, Parallel real-time monocular visual odometry, Proceedings of the 2013 IEEE International Conference on Robotics and Automation, ICRA 2013, pp.1-6, 2013.

C. Stachniss, Robotic mapping and exploration, 2009.

J. Stowers, M. Hayes, and A. Bainbridge-smith, Altitude control of a quadrotor helicopter using depth map from microsoft kinect sensor, Proceedings of the 2011 IEEE International Conference on Mechatronics, ICM 2011, pp.358-362, 2011.

H. Strasdat, J. Montiel, and A. J. Davison, Scale drift-aware large scale monocular-SLAM, Robotics: Science and Systems, vol.1, issue.1, pp.1-8, 2010.

J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, A benchmark for the evaluation of RGB-D SLAM systems, Proceedings of the 2012 IEEE International Conference on Intelligent Robots and Systems, IROS 2012, pp.573-580, 2012.

W. Tan, H. Liu, Z. Dong, G. Zhang, and H. Bao, Robust monocular-SLAM in dynamic environments, Proceedings of the 12th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp.209-218, 2013.

J. J. Tarrio and S. Pedre, Realtime edge-based visual odometry for a monocular camera, Proceedings of the 2015 IEEE International Conference on Computer Vision, ICCV 2015, pp.702-710, 2015.

K. Tateno, F. Tombari, I. Laina, and N. Navab, CNN-SLAM: Real-time dense monocular-SLAM with learned depth prediction, Proceedings of the 2017 IEEE International Conference on Computer Vision and Pattern Recognition, pp.1-8, 2017.

S. Thrun, D. Hahnel, D. Ferguson, M. Montemerlo, R. Triebel et al., A system for volumetric robotic mapping of abandoned mines, Proceedings of the 2003 IEEE International Conference on Robotics and Automation, ICRA 2003, pp.4270-4275, 2003.

S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron et al., Stanley: The robot that won the darpa grand challenge, Journal of field Robotics, vol.23, issue.9, pp.661-692, 2006.

C. Tomasi and T. Kanade, Detection and tracking of point features, 1991.

P. H. Torr and A. Zisserman, Mlesac: A new robust estimator with application to estimating image geometry, Computer vision and image understanding, vol.78, pp.138-156, 2000.

J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox et al., Depth Prediction Evaluation, 2017.

Y. Verdie, K. Yi, P. Fua, and V. Lepetit, Tilde: A temporally invariant learned detector, Proceedings of the 2015 IEEE International Conference on Computer Vision, pp.5279-5288, 2015.

, Vantage: Cutting edge, flagship camera with intelligent feedback and resolution, 2018.

G. Vogiatzis and C. Hernández, Video-based, real-time multi-view stereo, Image and Vision Computing, vol.29, pp.434-441, 2011.

Y. Wang, J. Fan, C. Qian, and L. Guo, Ego-motion estimation using sparse SUFR flow in monocular vision systems, International Journal of Advanced Robotic Systems, vol.13, issue.6, pp.1-9, 2016.

M. Weber, C. Rist, and J. M. Zöllner, Learning temporal features with CNNs for monocular visual ego motion estimation, Proceedings of the 2017 IEEE International Conference on Intelligent Transportation Systems, pp.1-6, 2017.

Z. Wei, D. J. Lee, and B. E. Nelson, FPGA-based real-time optical flow algorithm design and implementation, Journal of Multimedia, vol.2, issue.5, pp.38-45, 2007.

L. Yang, F. Tan, A. Li, Z. Cui, Y. Furukawa et al., Polarimetric dense monocular-SLAM, Proceedings of the 2018 IEEE International Conference on Computer Vision and Pattern Recognition, pp.3857-3866, 2018.

S. Yang, Y. Song, M. Kaess, and S. Scherer, Pop-up SLAM: Semantic monocular plane SLAM for low-texture environments, Proceedings of the 2016 IEEE International Conference on Intelligent Robots and Systems, IROS 2016, pp.1222-1229, 2016.

Z. Yang, P. Wang, W. Xu, L. Zhao, and R. Nevatia, Unsupervised learning of geometry with edge-aware depth-normal consistency, Proceedings of the The Thirty-Second AAAI Conference on Artificial Intelligence, AAAI-18, pp.1222-1229, 2017.

Z. Yang, P. Wang, Y. Wang, W. Xu, and R. Nevatia, Lego: Learning edge with geometry all at once by watching videos, Proceedings of the 2018 IEEE International Conference on Computer Vision and Pattern Recognition, pp.225-234, 2018.

J. Zhang and S. Singh, Visual-LiDAR odometry and mapping: Low-drift, robust, and fast, Proceedings of the 2015 IEEE International Conference on Robotics and Automation, ICRA 2015, pp.365-372, 2015.

L. Zhang and R. Koch, Hand-held monocular SLAM based on line segments, Proceedings of the 2011 Irish Machine Vision and Image Processing Conference, pp.7-14, 2011.

T. Zhou, M. Brown, N. Snavely, and D. G. Lowe, Unsupervised learning of depth and ego-motion from video, Proceedings of the 2017 IEEE International Conference on Computer Vision and Pattern Recognition, pp.7-12, 2017.

Y. Zou, Z. Luo, and J. B. Huang, Df-net: Unsupervised joint learning of depth and flow using cross-task consistency, Proceedings of the 2018 European Conference on Computer Vision, pp.38-55, 2018.

S. Zweig and L. Wolf, Interponet, a brain inspired neural network for optical flow dense interpolation, Proceedings of the 2017 IEEE International Conference on Computer Vision and Pattern Recognition, pp.79-86, 2017.