[1] |
Burri M, Oleynikova H, Achtelik M W, Siegwart R. Realtime visual-inertial mapping, re-localization and planning onboard MAVs in unknown environments. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hamburg, Germany: IEEE, 2015. 1872−1878 |
[2] |
Chatila R, Laumond J P. Position referencing and consistent world modeling for mobile robots. In: Proceedings of the 1985 IEEE International Conference on Robotics and Automation. Louis, Missouri, USA: IEEE, 1985. Vol. 2: 138−145 |
[3] |
Chatzopoulos D, Bermejo C, Huang Z, P Hui. Mobile augmented reality survey: From where we are to where we go. IEEE Access, 2017, 5: 6917−6950 doi: 10.1109/ACCESS.2017.2698164 |
[4] |
Taketomi T, Uchiyama H, Ikeda S. Visual SLAM algorithms: a survey from 2010 to 2016. Transactions on Computer Vision and Applications, 2017, 9(1): 16 doi: 10.1186/s41074-017-0027-2 |
[5] |
Strasdat H, Montiel J M M, Davison A J. Visual SLAM: Why filter? Image and Vision Computing, 2012, 30(2): 65−77 doi: 10.1016/j.imavis.2012.02.009 |
[6] |
Younes G, Asmar D, Shammas E, J Zelek. Keyframe-based monocular SLAM: Design, survey, and future directions. Robotics and Autonomous Systems, 2017, 98: 67−88 doi: 10.1016/j.robot.2017.09.010 |
[7] |
Olson C F, Matthies L H, Schoppers M, Maimore M W. Rover navigation using stereo ego-motion. Robotics and Autonomous Systems, 2003, 43(4): 215−229 doi: 10.1016/S0921-8890(03)00004-6 |
[8] |
Zhang Z. Microsoft kinect sensor and its effect. IEEE Multimedia, 2012, 19(2): 4−10 doi: 10.1109/MMUL.2012.24 |
[9] |
Huang A S, Bachrach A, Henry P, et al. Visual odometry and mapping for autonomous flight using an RGB-D camera. Robotics Research. Springer, Cham, 2017: 235−252 |
[10] |
Jones E S, Soatto S. Visual-inertial navigation, mapping and localization: A scalable real-time causal approach. The International Journal of Robotics Research, 2011, 30(4): 407−430 doi: 10.1177/0278364910388963 |
[11] |
Martinelli A. Vision and IMU data fusion: Closed-form solutions for attitude, speed, absolute scale, and bias determination. IEEE Transactions on Robotics, 2011, 28(1): 44−60 |
[12] |
Klein G, Murray D. Parallel tracking and mapping for small AR workspaces. In: Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality. Nara, Japan: IEEE, 2007. 1−10 |
[13] |
Mur-Artal R, Montiel J M M, Tardos J D. ORB-SLAM: A versatile and accurate monocular SLAM system. IEEE Transactions on Robotics, 2015, 31(5): 1147−1163 doi: 10.1109/TRO.2015.2463671 |
[14] |
Mur-Artal R, Tardós J D. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Transactions on Robotics, 2017, 33(5): 1255−1262 doi: 10.1109/TRO.2017.2705103 |
[15] |
Forster C, PizzoliM, Scaramuzza D. SVO: Fast semi-direct monocular visual odometry. In: Proceedings of the 2014 IEEE international conference on robotics and automation (ICRA). Hong Kong, China: IEEE, 2014. 15−22 |
[16] |
Engel J, Schops T, Cremers D. LSD-SLAM: Large-scale direct monocular SLAM. In: Proceedings of the 2014 European conference on computer vision. Zurich, Switzerland: Springer, 2014. 834−849 |
[17] |
Engel J, Stückler J, Cremers D. Large-scale direct SLAM with stereo cameras. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hamburg, Germany: IEEE, 2015. 1935−1942 |
[18] |
Li M, Mourikis A I. High-precision, consistent EKFbased visual-inertial odometry. The International Journal of Robotics Research, 2013, 32(6): 690−711 doi: 10.1177/0278364913481251 |
[19] |
Leutenegger S, Lynen S, Bosse M, Siegwart R, Furgale P. Keyframe-based visual inertial odometry using nonlinear optimization. The International Journal of Robotics Research, 2015, 34(3): 314−334 doi: 10.1177/0278364914554813 |
[20] |
Qin T, Li P, Shen S. Vins-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics, 2018, 34(4): 1004−1020 doi: 10.1109/TRO.2018.2853729 |
[21] |
Fossum E R. CMOS image sensors: Electronic camera-ona-chip. IEEE Transactions on Electron Devices, 1997, 44(10): 1689−1698 doi: 10.1109/16.628824 |
[22] |
Delbruck T. Neuromorophic vision sensing and processing. In: Proceedings of the 46th European SolidState Device Research Conference (ESSDERC). Lansanne, Switzerland: IEEE, 2016. 7−14 |
[23] |
Delbruck T, Lichtsteiner P. Fast sensory motor control based on event-based hybrid neuromorphic-procedural system. In: Proceedings of the IEEE International Symposium on Circuits and Systems. New Orleans, USA: IEEE, 2007. 845−848 |
[24] |
Delbruck T, Lang M. Robotic goalie with 3 ms reaction time at 4% CPU load using event-based dynamic vision sensor. Frontiers in Neuroscience, 2013, 7: 223 |
[25] |
Glover A, Bartolozzi C. Event-driven ball detection and gaze fixation in clutter. In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon, Korea: IEEE, 2016. 2203−2208 |
[26] |
Benosman R, Ieng S H, Clercq C, Bartolozzi C, Srinivasan M. Asynchronous frameless event-based optical flow. Neural Networks, 2012, 27: 32−37 doi: 10.1016/j.neunet.2011.11.001 |
[27] |
Benosman R, Clercq C, Lagorce X, leng S H, Bartolozzi C. Event-based visual flow. IEEE Transactions on Neural Networks and Learning Systems, 2013, 25(2): 407−417 |
[28] |
Rueckauer B, Delbruck T. Evaluation of event-based algorithms for optical flow with ground-truth from inertial measurement sensor. Frontiers in Neuroscience, 2016, 10: 176 |
[29] |
Bardow P, Davison A J, Leutenegger S. Simultaneous optical flow and intensity estimation from an event camera. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. LAS VEGAS, USA: IEEE, 2016. 884−892 |
[30] |
Reinbacher C, Graber G, Pock T. Real-time intensityimage reconstruction for event cameras using manifold regularisation. International Journal of Computer Vision, 2018, 126(12): 1381−1393 doi: 10.1007/s11263-018-1106-2 |
[31] |
Mahowald M. VLSI analogs of neuronal visual processing: A synthesis of form and function. California Institute of Technology, 1992. |
[32] |
Posch C, Serrano-Gotarredona T, Linares-Barranco B, Delbruck T. Retinomorphic event-based vision sensors: Bioinspired cameras with spiking output. Proceedings of the IEEE, 2014, 102(10): 1470−1484 doi: 10.1109/JPROC.2014.2346153 |
[33] |
Lichtsteiner P, Posch C, Delbruck T. A 128×128 120 db 30 mw asynchronous vision sensor that responds to relative intensity change. In: Proceedings of the 2006 IEEE International Solid State Circuits Conference-Digest of Technical Papers. San Francisco, CA, USA: IEEE, 2006. 2060−2069 |
[34] |
Lichtsteiner P, Posch C, Delbruck T. A 128×128 120 dB 15 μs Latency Asynchronous Temporal Contrast Vision Sensor. IEEE Journal of Solid-State Circuits, 2008, 43(2): 566−576 doi: 10.1109/JSSC.2007.914337 |
[35] |
Son B, Suh Y, Kim S, et al. 4. 1 A 640×480 dynamic vision sensor with a 9 μm pixel and 300 Meps address-event representation. In: Proceedings of the 2017 IEEE International Solid-State Circuits Conference (ISSCC). San Francisco, CA, USA: IEEE, 2017. 66−67 |
[36] |
Posch C, Matolin D, Wohlgenannt R. A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS. IEEE Journal of Solid-State Circuits, 2010, 46(1): 259−275 |
[37] |
Posch C, Matolin D, Wohlgenannt R. A QVGA 143 dB dynamic range asynchronous address-event PWM dynamic image sensor with lossless pixel-level video compression. In: Proceedings of the 2010 IEEE International Solid-State Circuits Conference-(ISSCC). San Francisco, CA, USA: IEEE, 2010. 400−401 |
[38] |
Berner R, Brandli C, Yang M, Liu S C, Delbruck T. A 240×180 120 db 10 mw 12 us-latency sparse output vision sensor for mobile applications. In: Proceedings of the International Image Sensors Workshop. Snowbird, Utah, USA: IEEE, 2013. 41−44 |
[39] |
Brandli C, Berner R, Yang M, Liu S C, Delbruck T. A 240×180 130 db 3 μs latency global shutter spatiotemporal vision sensor. IEEE Journal of Solid-State Circuits, 2014, 49(10): 2333−2341 doi: 10.1109/JSSC.2014.2342715 |
[40] |
Guo M, Huang J, Chen S. Live demonstration: A 768×640 pixels 200 Meps dynamic vision sensor. In: Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS). Baltimore, Maryland, USA: IEEE, 2017. 1−1 |
[41] |
Li C, Brandli C, Berner R, et al. Design of an RGBW color VGA rolling and global shutter dynamic and active-pixel vision sensor. In: Proceedings of the 2015 IEEE International Symposium on Circuits and Systems (ISCAS). Liston, Portulgal: IEEE, 2015. 718−721 |
[42] |
Moeys D P, Li C, Martel J N P, et al. Color temporal contrast sensitivity in dynamic vision sensors. In: Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS). Baltimore, Maryland, USA: IEEE, 2017. 1−4 |
[43] |
Marcireau A, Ieng S H, Simon-Chane C, Benosman R B. Event-based color segmentation with a high dynamic range sensor. Frontiers in Neuroscience, 2018, 12: 135 doi: 10.3389/fnins.2018.00135 |
[44] |
Weikersdorfer D, Conradt J. Event-based particle filtering for robot self-localization. In: Proceedings of the 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO). Guangzhou, China: IEEE, 2012. 866−870 |
[45] |
Weikersdorfer D, Hoffmann R, Conradt J. Simultaneous localization and mapping for event-based vision systems. In: Proceedings of the 2013 International Conference on Computer Vision Systems. St. Petersburg, Russia: Springer, 2013. 133−142 |
[46] |
Hoffmann R, Weikersdorfer D, Conradt J. Autonomous indoor exploration with an event-based visual SLAM system. In: Proceedings of the 2013 European Conference on Mobile Robots. Barcelona, Catalonia, Spain: IEEE, 2013. 38−43 |
[47] |
Mueggler E, Huber B, Scaramuzza D. Event-based, 6-DOF pose tracking for high-speed maneuvers. In: Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, USA: IEEE, 2014. 2761−2768 |
[48] |
Kim H, Leutenegger S, Davison A J. Real-time 3D reconstruction and 6-DoF tracking with an event camera. In: Proceedings of the 2016 European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 349−364 |
[49] |
Rebecq H, Horstschafer T, Gallego G, Scaramuzza D. EVO: A geometric approach to event-based 6-DOF parallel tracking and mapping in real time. IEEE Robotics and Automation Letters, 2016, 2(2): 593−600 |
[50] |
Rebecq H, Gallego G, Scaramuzza D. EMVS: Event-based multi-view stereo. In: Proceedings of the 2016 British Machine Vision Conference (BMVC). York, UK: Springer, 2016(CONF). |
[51] |
Bryner S, Gallego G, Rebecq H, Scaramuzza D. Eventbased, direct camera tracking from a photometric 3D map using nonlinear optimization. In: the 2019 International Conference on Robotics and Automation. Montreal, Canada: IEEE, 2019. 2 |
[52] |
Censi A, Scaramuzza D. Low-latency event-based visual odometry. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China: IEEE, 2014. 703−710 |
[53] |
Weikersdorfer D, Adrian D B, Cremers D, Conradt J. Eventbased 3D SLAM with a depth-augmented dynamic vision sensor. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China: IEEE, 2014. 359−364 |
[54] |
Tedaldi D, Gallego G, Mueggler E, Scaramuzza D. Feature detection and tracking with the dynamic and active-pixel vision sensor (DAVIS). In: Proceedings of the 2016 Second International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP). Krakow, Poland: IEEE, 2016. 1−7 |
[55] |
Kueng B, Mueggler E, Gallego G, Scaramuzza D. Lowlatency visual odometry using event-based feature tracks. In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon, Korea: IEEE, 2016. 16−23 |
[56] |
Zhu A Z, Atanasov N, Daniilidis K. Event-based visual inertial odometry. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, Hawaii, USA: IEEE, 2017. 5816−5824 |
[57] |
Zhu A Z, Atanasov N, Daniilidis K. Event-based feature tracking with probabilistic data association. In: Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA). Marina Bay, Singapore: IEEE, 2017. 4465−4470 |
[58] |
Mourikis A I, Roumeliotis S I. A multi-state constraint Kalman filter for vision-aided inertial navigation. In: Proceedings of the 2007 IEEE International Conference on Robotics and Automation (ICRA). Roma, Italy: IEEE, 2007. 3565−3572 |
[59] |
Rebecq H, Horstschaefer T, Scaramuzza D. Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization. In: Proceedings of the 2017 British Machine Vision Conference (BMVC). London, UK: Springer, 2017(CONF). |
[60] |
Gallego G, Scaramuzza D. Accurate angular velocity estimation with an event cameras. IEEE Robotics and Automation Letters, 2017, 2(2): 632−639 doi: 10.1109/LRA.2016.2647639 |
[61] |
Rosten E, Drummond T. Machine learning for high-speed corner detection. In: Proceedings of the 2006 European Conference on Computer Vision. Graz, Austria: Springer, 2006. 430−443 |
[62] |
Lucas B D, Kanade T. An Iterative Image Registration Technique with An Application to Stereo Vision. 1981. 121−130 |
[63] |
Leutenegger S, Furgale P, Rabaud V, et al. Keyframe-based visual-inertial slam using nonlinear optimization. In: Proceedings of the 2013 Robotis Science and Systems (RSS). Berlin, German, 2013. |
[64] |
Vidal A R, Rebecq H, Horstschaefer T, Scaramuzza D. Ultimate SLAM? Combining events, images, and IMU for robust visual SLAM in HDR and high-speed scenarios. IEEE Robotics and Automation Letters, 2018, 3(2): 994−1001 doi: 10.1109/LRA.2018.2793357 |
[65] |
Mueggler E, Gallego G, Rebecq H, Scaramuzza D. Continuous-time visual-inertial odometry for event cameras. IEEE Transactions on Robotics, 2018, 34(6): 1425−1440 doi: 10.1109/TRO.2018.2858287 |
[66] |
Mueggler E, Gallego G, Scaramuzza D. Continuous-time trajectory estimation for event-based vision sensors. In: Proceedings of Robotics: Science and Systems XI (RSS). Rome, Italy: 2015. DOI: 10.15607/RSS.2015.XI.036 |
[67] |
Patron-Perez A, Lovegrove S, Sibley G. A spline-based trajectory representation for sensor fusion and rolling shutter cameras. International Journal of Computer Vision, 2015, 113(3): 208−219 doi: 10.1007/s11263-015-0811-3 |
[68] |
Barranco F, Fermuller C, Aloimonos Y, Delbruck T. A dataset for visual navigation with neuromorphic methods. Frontiers in Neuroscience, 2016, 10: 49 |
[69] |
Mueggler E, Rebecq H, Gallego G, Delbruck T, Scaramuzza D. The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM. The International Journal of Robotics Research, 2017, 36(2): 142−149 doi: 10.1177/0278364917691115 |
[70] |
Binas J, Neil D, Liu S C, Delbruck T. DDD17: End-to-end DAVIS driving dataset. arXiv: 1711. 01458, 2017 |
[71] |
Zhu A Z, Thakur D, Ozaslan T, Pfrommer B, Kumar V, Daniilidis K. The multivehicle stereo event camera dataset: An event camera dataset for 3D perception. IEEE Robotics and Automation Letters, 2018, 3(3): 2032−2039 doi: 10.1109/LRA.2018.2800793 |
[72] |
Leung S, Shamwell E J, Maxey C, Nothwang W D. Toward a large-scale multimodal event-based dataset for neuromorphic deep learning applications. In: Proceedings of the 2018 Micro-and Nanotechnology Sensors, Systems, and Applications X. International Society for Optics and Photonics. Orlando, Florida, USA: SPIE, 2018. 10639: 106391T |
[73] |
Mitrokhin A, Ye C, Fermuller C, Aloimonos Y, Delbruck T. EV-IMO: Motion segmentation dataset and learning pipeline for event cameras. arXiv: 1903. 07520, 2019 |