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In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimation.
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2018
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M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 3213–3223
2016
Cited alongside, same era.
V. Usenko, J. Engel, J. Stückler, and D. Cremers, “Direct visual-inertial odometry with stereo cameras,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 1885–1892
2016
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A. Concha, G. Loianno, V. Kumar, and J. Civera, “Visual-inertial direct slam,” in 2016 IEEE international conference on robotics and automation (ICRA) . IEEE, 2016, pp. 1331–1338
2016
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I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, “Deeper depth prediction with fully convolutional residual networks,” in 2016 Fourth international conference on 3D vision (3DV) . IEEE, 2016, pp. 239–248
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2016
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R. Garg, V. K. BG, G. Carneiro, and I. Reid, “Unsupervised cnn for single view depth estimation: Geometry to the rescue,” in European Conference on Computer Vision . Springer, 2016, pp. 740–756
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T. Qin and S. Shen, “Online temporal calibration for monocular visual-inertial systems,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 3662–3669
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M. Turan, Y. Almalioglu, H. B. Gilbert, A. E. Sari, U. Soylu, and M. Sitti, “Endo-vmfusenet: A deep visual-magnetic sensor fusion approach for endoscopic capsule robots,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1–7
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2018
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J. Delmerico and D. Scaramuzza, “A benchmark comparison of monocular visual-inertial odometry algorithms for flying robots,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 2502–2509
2018
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H. Zhan, R. Garg, C. Saroj Weerasekera, K. Li, H. Agarwal, and I. Reid, “Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 340–349
2018
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J. Wulff and M. J. Black, “Temporal interpolation as an unsupervised pretraining task for optical flow estimation,” in German Conference on Pattern Recognition . Springer, 2018, pp. 567–582
2018
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R. Li, S. Wang, Z. Long, and D. Gu, “Undeepvo: Monocular visual odometry through unsupervised deep learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 7286–7291
2018
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E. J. Shamwell, S. Leung, and W. D. Nothwang, “Vision-aided absolute trajectory estimation using an unsupervised deep network with online error correction,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 2524–2531
2018
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R. Clark, M. Bloesch, J. Czarnowski, S. Leutenegger, and A. J. Davison, “Learning to solve nonlinear least squares for monocular stereo,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 284–299
2018
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Y. Zou, Z. Luo, and J.-B. Huang, “Df-net: Unsupervised joint learning of depth and flow using cross-task consistency,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 36–53
2018
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M. Turan, Y. Almalioglu, H. B. Gilbert, F. Mahmood, N. J. Durr, H. Araujo, A. E. Sarı, A. Ajay, and M. Sitti, “Learning to navigate endoscopic capsule robots,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 3075–3082, 2019
2019
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Y. Almalioglu, M. R. U. Saputra, P. P. de Gusmao, A. Markham, and N. Trigoni, “GANVO: Unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 5474–5480
2019
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Y. Yang, P. Geneva, K. Eckenhoff, and G. Huang, “Degenerate motion analysis for aided ins with online spatial and temporal sensor calibration,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 2070–2077, 2019
2019
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Z. Wu, X. Wu, X. Zhang, S. Wang, and L. Ju, “Spatial correspondence with generative adversarial network: Learning depth from monocular videos,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 7494–7504
2019
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E. J. Shamwell, K. Lindgren, S. Leung, and W. D. Nothwang, “Unsupervised deep visual-inertial odometry with online error correction for rgb-d imagery,” IEEE transactions on pattern analysis and machine intelligence , pp. 1–1, 2019, early-access. [Online]. Available: https://doi.org/10.1109/tpami.2019.2909895
2019
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2019
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C. Chen, S. Rosa, Y. Miao, C. X. Lu, W. Wu, A. Markham, and N. Trigoni, “Selective sensor fusion for neural visual-inertial odometry,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 542–10 551
2019
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A. Ranjan, V. Jampani, L. Balles, K. Kim, D. Sun, J. Wulff, and M. J. Black, “Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 240–12 249
2019
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2019
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M. Jaderberg, K. Simonyan, A. Zisserman et al. , “Spatial transformer networks,” in Advances in neural information processing systems , 2015, pp. 2017–2025
2025
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S. Wang, R. Clark, H. Wen, and N. Trigoni, “Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on . IEEE, 2017, pp. 2043–2050
2050
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