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This paper presents an end-to-end multi-modal learning approach for monocular Visual-Inertial Odometry (VIO), which is specifically designed to exploit sensor complementarity in the light of sensor degradation scenarios.
A practical bayesian framework for backpropagation networks
MacKay, D. J · 1992
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Visual-inertial telepresence for aerial manipulation
Lee, J., Balachandran, R., Sarkisov, Y. S., De Stefano, M., Coelho, A., Shinde, K., Kim, M. J., Triebel, R., and Kondak, K · 2003
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Lee, J., Humt, M., Feng, J., and Triebel, R · 2006
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Analysis and modeling of inertial sensors using allan variance
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Geiger, A., Lenz, P., Stiller, C., and Urtasun, R · 2013
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Occlusion-aware depth estimation using light-field cameras
Wang, T.-C., Efros, A. A., and Ramamoorthi, R · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Modelling uncertainty in deep learning for camera relocalization
Kendall, A. and Cipolla, R · 2016
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Geometric loss functions for camera pose regression with deep learning
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Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q. V., Hinton, G. E., and Dean, J · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Image-based localization using lstms for structured feature correlation
Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., and Cremers, D · 2017
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Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks
Wang, S., Clark, R., Wen, H., and Trigoni, N · 2017
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Unsupervised learning of depth and ego-motion from video
Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Yin, Z. and Shi, J · 2018
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Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
Zhan, H., Garg, R., Saroj Weerasekera, C., Li, K., Agarwal, H., and Reid, I · 2018
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Selfvio: Self-supervised deep monocular visual-inertial odometry and depth estimation
Almalioglu, Y., Turan, M., Sari, A. E., Saputra, M. R. U., de Gusmão, P. P., Markham, A., and Trigoni, N · 2019
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nuscenes: A multimodal dataset for autonomous driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O · 2019
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Selective sensor fusion for neural visual-inertial odometry
Chen, C., Rosa, S., Miao, Y., Lu, C. X., Wu, W., Markham, A., and Trigoni, N · 2019
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Ionet: Learning to cure the curse of drift in inertial odometry
Chen, C., Lu, X., Markham, A., and Trigoni, N · 2018
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Li, R., Wang, S., Long, Z., and Gu, D · 2018
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A scalable laplace approximation for neural networks
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Sun, D., Yang, X., Liu, M.-Y., and Kautz, J · 2018
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End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
Wang, S., Clark, R., Wen, H., and Trigoni, N · 2018
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Han, L., Lin, Y., Du, G., and Lian, S · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Scalability in perception for autonomous driving: Waymo open dataset, 2019
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., Vasudevan, V., Han, W., Ngiam, J., Zhao, H., Timofeev, A., Ettinger, S., Krivokon, M., Gao, A., Joshi, A., Zhang, Y., Shlens, J., Chen, Z., and Anguelov, D · 2019
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Multiplicative interactions and where to find them
Jayakumar, S. M., Czarnecki, W. M., Menick, J., Schwarz, J., Rae, J., Osindero, S., Teh, Y. W., Harley, T., and Pascanu, R · 2020
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Ardea—an mav with skills for future planetary missions
Lutz, P., Müller, M. G., Maier, M., Stoneman, S., Tomić, T., von Bargen, I., Schuster, M. J., Steidle, F., Wedler, A., Stürzl, W., et al · 2020
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D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry
Yang, N., Stumberg, L. v., Wang, R., and Cremers, D · 2020
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