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This paper presents an self-supervised deep learning network for monocular visual inertial odometry (named DeepVIO).
On-manifold preintegration for real-time visual-inertial odometry
Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza · 1906
Earlier work this paper cites.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Daniel Scharstein and Richard Szeliski · 2002
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A multi-state constraint kalman filter for vision-aided inertial navigation
Anastasios I Mourikis and Stergios I Roumeliotis · 2007
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Efficient dense scene flow from sparse or dense stereo data
Andreas Wedel, Clemens Rabe, Tobi Vaudrey, Thomas Brox, Uwe Franke, and Daniel Cremers · 2008
Earlier work this paper cites.
Visual odometry: Part ii: Matching, robustness, optimization, and applications
Friedrich Fraundorfer and Davide Scaramuzza · 2012
Earlier work this paper cites.
Keyframe-based visual-inertial slam using nonlinear optimization
Stefan Leutenegger, Paul Furgale, Vincent Rabaud, Margarita Chli, Kurt Konolige, and Roland Siegwart · 2013
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Robust visual inertial odometry using a direct ekf-based approach
Michael Bloesch, Sammy Omari, Marco Hutter, and Roland Siegwart · 2015
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
Earlier work this paper cites.
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, José Neira, Ian Reid, and John J Leonard · 2016
Earlier work this paper cites.
Jakob Engel, Vladlen Koltun, and Daniel Cremers · 2016
Earlier work this paper cites.
Visual-inertial monocular slam with map reuse
Raul Mur-Artal and Juan Domingo Tardos · 2016
Earlier work this paper cites.
Learning to fuse: A deep learning approach to visual-inertial camera pose estimation
Jason R Rambach, Aditya Tewari, Alain Pagani, and Didier Stricker · 2016
Cited alongside, same era.
Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, Vijay Kumar BG, Gustavo Carneiro, and Ian Reid · 2016
Cited alongside, same era.
Go-icp: A globally optimal solution to 3d icp point-set registration
Jiaolong Yang, Hongdong Li, Dylan Campbell, and Yunde Jia · 2016
Cited alongside, same era.
Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras
Raul Mur-Artal and Juan D Tardós · 2017
Cited alongside, same era.
Vins-mono: A robust and versatile monocular visual-inertial state estimator
Qin Tong, Peiliang Li, and Shaojie Shen · 2017
Cited alongside, same era.
Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks
End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
Sen Wang, Ronald Clark, Hongkai Wen, and Niki Trigoni · 2018
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Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Zhichao Yin and Jianping Shi · 2018
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Vision-aided absolute trajectory estimation using an unsupervised deep network with online error correction
E Jared Shamwell, Sarah Leung, and William D Nothwang · 2018
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Ongoing evolution of visual slam from geometry to deep learning: Challenges and opportunities
Ruihao Li, Sen Wang, and Dongbing Gu · 2018
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Vincent Casser, Soeren Pirk, Reza Mahjourian, and Anelia Angelova · 2018
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Sen Wang, Ronald Clark, Hongkai Wen, and Niki Trigoni · 2017
Cited alongside, same era.
Vinet: Visual-inertial odometry as a sequence-to-sequence learning problem
Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, and Niki Trigoni · 2017
Cited alongside, same era.
Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
Cited alongside, same era.
Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 2017
Cited alongside, same era.
Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
Cited alongside, same era.
Robust stereo visual inertial odometry for fast autonomous flight
Ke Sun, Kartik Mohta, Bernd Pfrommer, Michael Watterson, Sikang Liu, Yash Mulgaonkar, Camillo J Taylor, and Vijay Kumar · 2018
Cited alongside, same era.
Df-net: Unsupervised joint learning of depth and flow using cross-task consistency
Yuliang Zou, Zelun Luo, and Jia-Bin Huang · 2018
Later among the works it cites.
Undeepvo: Monocular visual odometry through unsupervised deep learning
Ruihao Li, Sen Wang, Zhiqiang Long, and Dongbing Gu · 2018
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Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera, Kejie Li, Harsh Agarwal, and Ian Reid · 2018
Later among the works it cites.
Pyramid stereo matching network
Jia-Ren Chang and Yong-Sheng Chen · 2018
Later among the works it cites.
Learning monocular visual odometry with dense 3d mapping from dense 3d flow
Zhao Cheng, Sun Li, Pulak Purkait, Tom Duckett, and Rustam Stolkin · 2018
Later among the works it cites.