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We present the first learning-based visual odometry (VO) model, which generalizes to multiple datasets and real-world scenarios and outperforms geometry-based methods in challenging scenes.
An efficient solution to the five-point relative pose problem
D. Nistér · 2004
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Memory-based learning for visual odometry
R. Roberts, H. Nguyen, N. Krishnamurthi, and T. Balch · 2008
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Semi-parametric models for visual odometry
V. Guizilini and F. Ramos · 2012
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Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
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LSD-SLAM: Large-scale direct monocular slam
J. Engel, T. Schops, and D. Cremers · 2014
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Svo: Fast semi-direct monocular visual odometry
C. Forster, M. Pizzoli, and D. Scaramuzza · 2014
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Evaluation of non-geometric methods for visual odometry
T. A. Ciarfuglia, G. Costante, P. Valigi, and E. Ricci · 2014
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Visual simultaneous localization and mapping: a survey
J. Fuentes-Pacheco, J. Ruiz-Ascencio, and J. M. Rendón-Mancha · 2015
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Orb-slam: a versatile and accurate monocular slam system
R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos · 2015
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High accuracy monocular SFM and scale correction for autonomous driving
S. Song, M. Chandraker, and C. Guest · 2015
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Exploring representation learning with cnns for frame-to-frame ego-motion estimation
G. Costante, M. Mancini, P. Valigi, and T. A. Ciarfuglia · 2016
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The euroc micro aerial vehicle datasets
M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder, S. Omari, M. W. Achtelik, and R. Siegwart · 2016
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Svo: Semidirect visual odometry for monocular and multicamera systems
C. Forster, Z. Zhang, M. Gassner, M. Werlberger, and D. Scaramuzza · 2016
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Direct sparse odometry
J. Engel, V. Koltun, and D. Cremers · 2017
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Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe · 2017
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Sfm-net: Learning of structure and motion from video
S. Vijayanarasimhan, S. Ricco, C. Schmidy, R. Sukthankar, and K. Fragkiadaki · 2017
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Keyframe-based monocular slam: design, survey, and future directions
G. Younes, D. Asmar, E. Shammas, and J. Zelek · 2017
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Cnn-slam: Real-time dense monocular slam with learned depth prediction
K. Tateno, F. Tombari, I. Laina, and N. Navab · 2017
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Demon: Depth and motion network for learning monocular stereo
B. Ummenhofer, H. Zhou, J. Uhrig, N. Mayer, E. Ilg, A. Dosovitskiy, and T. Brox · 2017
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Cnn-slam: Real-time dense monocular slam with learned depth prediction
Ls-net: Learning to solve nonlinear least squares for monocular stereo
R. Clark, M. Bloesch, J. Czarnowski, S. Leutenegger, and A. J. Davison · 2018
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Learning depth from monocular videos using direct methods
C. Wang, J. M. Buenaposada, R. Zhu, and S. Lucey · 2018
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Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
R. Mahjourian, M. Wicke, and A. Angelova · 2018
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Training deep networks with synthetic data: Bridging the reality gap by domain randomization
J. Tremblay, A. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield · 2018
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz · 2018
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K. Tateno, F. Tombari, I. Laina, and N. Navab · 2017
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks
S. Wang, R. Clark, H. Wen, and N. Trigoni · 2017
Cited alongside, same era.
End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
S. Wang, R. Clark, H. Wen, and N. Trigoni · 2018
Cited alongside, same era.
Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Z. Yin and J. Shi · 2018
Cited alongside, same era.
Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
H. Zhan, R. Garg, C. S. Weerasekera, K. Li, H. Agarwal, and I. Reid · 2018
Cited alongside, same era.
Undeepvo: Monocular visual odometry through unsupervised deep learning
R. Li, S. Wang, Z. Long, and D. Gu · 2018
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Improving learning-based ego-motion estimation with homomorphism-based losses and drift correction
X. Wang, D. Maturana, S. Yang, W. Wang, Q. Chen, and S. Scherer · 2019
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Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation
A. Ranjan, V. Jampani, L. Balles, K. Kim, D. Sun, J. Wulff, and M. J. Black · 2019
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Unsupervised collaborative learning of keyframe detection and visual odometry towards monocular deep slam
L. Sheng, D. Xu, W. Ouyang, and X. Wang · 2019
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Unos: Unified unsupervised optical-flow and stereo-depth estimation by watching videos
Y. Wang, P. Wang, Z. Yang, C. Luo, Y. Yang, and W. Xu · 2019
Later among the works it cites.
Tartanair: A dataset to push the limits of visual slam
W. Wang, D. Zhu, X. Wang, Y. Hu, Y. Qiu, C. Wang, Y. Hu, A. Kapoor, and S. Scherer · 2020
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D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry
N. Yang, L. v. Stumberg, R. Wang, and D. Cremers · 2020
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Robust and efficient estimation of absolute camera pose for monocular visual odometry
H. Li, W. Chen, j. Zhao, J.-C. Bazin, L. Luo, Z. Liu, and Y.-H. Liu · 2020
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Visual odometry revisited: What should be learnt?
H. Zhan, C. S. Weerasekera, J.-W. Bian, and I. Reid · 2020
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Self-supervised deep visual odometry with online adaptation
S. Li, X. Wang, Y. Cao, F. Xue, Z. Yan, and H. Zha · 2020
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