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The ability for an agent to localize itself within an environment is crucial for many real-world applications.
On the shortest spanning subtree of a graph and the traveling salesman problem
J. B. Kruskal · 1956
Earlier work this paper cites.
Shortest connection networks and some generalizations
R. C. Prim · 1957
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A note on two problems in connexion with graphs
E. W. Dijkstra · 1959
Earlier work this paper cites.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
M. A. Fischler and R. C. Bolles · 1987
Earlier work this paper cites.
Probabilistic robotics
S. Thrun, W. Burgard, and D. Fox · 2005
Earlier work this paper cites.
A multi-state constraint kalman filter for vision-aided inertial navigation
A. I. Mourikis and S. I. Roumeliotis · 2007
Earlier work this paper cites.
g 2 o: A general framework for graph optimization
R. Kümmerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard · 2011
Earlier work this paper cites.
Kinectfusion: Real-time dense surface mapping and tracking
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon · 2011
Earlier work this paper cites.
Factor graphs and gtsam: A hands-on introduction
F. Dellaert · 2012
Earlier work this paper cites.
Bags of binary words for fast place recognition in image sequences
D. Gálvez-López and J. D. Tardos · 2012
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isam2: Incremental smoothing and mapping using the bayes tree
M. Kaess, H. Johannsson, R. Roberts, V. Ila, J. J. Leonard, and F. Dellaert · 2012
Earlier work this paper cites.
On the consistency of vision-aided inertial navigation
D. G. Kottas, J. A. Hesch, S. L. Bowman, and S. I. Roumeliotis · 2013
Earlier work this paper cites.
Slam++: Simultaneous localisation and mapping at the level of objects
R. F. Salas-Moreno, R. A. Newcombe, H. Strasdat, P. H. Kelly, and A. J. Davison · 2013
Earlier work this paper cites.
Lsd-slam: Large-scale direct monocular slam
J. Engel, T. Schöps, and D. Cremers · 2014
Earlier work this paper cites.
Svo: Fast semi-direct monocular visual odometry
C. Forster, M. Pizzoli, and D. Scaramuzza · 2014
Earlier work this paper cites.
Camera-imu-based localization: Observability analysis and consistency improvement
J. A. Hesch, D. G. Kottas, S. L. Bowman, and S. I. Roumeliotis · 2014
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
Earlier work this paper cites.
Posenet: A convolutional network for real-time 6-dof camera relocalization
A. Kendall, M. Grimes, and R. Cipolla · 2015
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Keyframe-based visual–inertial odometry using nonlinear optimization
S. Leutenegger, S. Lynen, M. Bosse, R. Siegwart, and P. Furgale · 2015
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Deep convolutional neural fields for depth estimation from a single image
F. Liu, C. Shen, and G. Lin · 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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Playing doom with slam-augmented deep reinforcement learning
S. Bhatti, A. Desmaison, O. Miksik, N. Nardelli, N. Siddharth, and P. H. Torr · 2016
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Probabilistic data association for semantic slam
S. L. Bowman, N. Atanasov, K. Daniilidis, and G. J. Pappas · 2017
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Dsac-differentiable ransac for camera localization
E. Brachmann, A. Krull, S. Nowozin, J. Shotton, F. Michel, S. Gumhold, and C. Rother · 2017
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Vinet: Visual-inertial odometry as a sequence-to-sequence learning problem
R. Clark, S. Wang, H. Wen, A. Markham, and N. Trigoni · 2017
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Svo: Semidirect visual odometry for monocular and multicamera systems
C. Forster, Z. Zhang, M. Gassner, M. Werlberger, and D. Scaramuzza · 2017
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Cognitive mapping and planning for visual navigation
S. Gupta, J. Davidson, S. Levine, R. Sukthankar, and J. Malik · 2017
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Playing fps games with deep reinforcement learning
G. Lample and D. S. Chaplot · 2017
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C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard · 2016
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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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Deep image homography estimation
D. DeTone, T. Malisiewicz, and A. Rabinovich · 2016
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Backprop kf: Learning discriminative deterministic state estimators
T. Haarnoja, A. Ajay, S. Levine, and P. Abbeel · 2016
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Vizdoom: A doom-based ai research platform for visual reinforcement learning
M. Kempka, M. Wydmuch, G. Runc, J. Toczek, and W. Jaśkowski · 2016
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Learning to navigate in complex environments
P. Mirowski, R. Pascanu, F. Viola, H. Soyer, A. Ballard, A. Banino, M. Denil, R. Goroshin, L. Sifre, K. Kavukcuoglu, et al · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Semanticfusion: Dense 3d semantic mapping with convolutional neural networks
J. McCormac, A. Handa, A. Davison, and S. Leutenegger · 2017
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Relative camera pose estimation using convolutional neural networks
I. Melekhov, J. Ylioinas, J. Kannala, and E. Rahtu · 2017
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Neural map: Structured memory for deep reinforcement learning
E. Parisotto and R. Salakhutdinov · 2017
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Benchmarking 6dof urban visual localization in changing conditions
T. Sattler, W. Maddern, A. Torii, J. Sivic, T. Pajdla, M. Pollefeys, and M. Okutomi · 2017
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Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation
L. Tai, G. Paolo, and M. Liu · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks
S. Wang, R. Clark, H. Wen, and N. Trigoni · 2017
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J. Zhang, L. Tai, J. Boedecker, W. Burgard, and M. Liu · 2017
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Target-driven visual navigation in indoor scenes using deep reinforcement learning
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi · 2017
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Active neural localization
D. S. Chaplot, E. Parisotto, and R. Salakhutdinov · 2018
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