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This paper introduces a novel proprioceptive state estimator for legged robots that combines model-based filters and deep neural networks.
M. Bloesch et al. , “State estimation for legged robots: Consistent fusion of leg kinematics and imu,” in Robotics: science and systems , vol. 17. MIT Press, 2012, pp. 17–24
2012
Earlier work this paper cites.
M. Bloesch, C. Gehring, P. Fankhauser, M. Hutter, M. A. Hoepflinger, and R. Siegwart, “State estimation for legged robots on unstable and slippery terrain,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 6058–6064
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
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2014
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2014
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A. Barrau and S. Bonnabel, “The invariant extended kalman filter as a stable observer,” IEEE Transactions on Automatic Control , vol. 62, no. 4, pp. 1797–1812, 2016
2016
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2017
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2017
Earlier work this paper cites.
R. Hartley et al. , “Legged robot state-estimation through combined forward kinematic and preintegrated contact factors,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 4422–4429
2018
Earlier work this paper cites.
C. Chen, X. Lu, A. Markham, and N. Trigoni, “Ionet: Learning to cure the curse of drift in inertial odometry,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
A. Antonini, W. Guerra, V. Murali, T. Sayre-McCord, and S. Karaman, “The blackbird dataset: A large-scale dataset for uav perception in aggressive flight,” in International Symposium on Experimental Robotics . Springer, 2018, pp. 130–139
2018
Earlier work this paper cites.
J. Hwangbo, J. Lee, and M. Hutter, “Per-contact iteration method for solving contact dynamics,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 895–902, 2018
2018
Earlier work this paper cites.
Z. Zhang and D. Scaramuzza, “A tutorial on quantitative trajectory evaluation for visual (-inertial) odometry,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 7244–7251
2018
Cited alongside, same era.
R. Hartley, M. Ghaffari, R. M. Eustice, and J. W. Grizzle, “Contact-aided invariant extended kalman filtering for robot state estimation,” The International Journal of Robotics Research , vol. 39, no. 4, pp. 402–430, 2020
2020
Cited alongside, same era.
S. Herath, H. Yan, and Y. Furukawa, “Ronin: Robust neural inertial navigation in the wild: Benchmark, evaluations, & new methods,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 3146–3152
2020
Cited alongside, same era.
M. Brossard, S. Bonnabel, and A. Barrau, “Denoising imu gyroscopes with deep learning for open-loop attitude estimation,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 4796–4803, 2020
2020
2021
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S. Mysore, B. Mabsout, R. Mancuso, and K. Saenko, “Regularizing action policies for smooth control with reinforcement learning,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 1810–1816
2021
Later among the works it cites.
S. Hong, Y. Um, J. Park, and H.-W. Park, “Agile and versatile climbing on ferromagnetic surfaces with a quadrupedal robot,” Science Robotics , vol. 7, no. 73, p. eadd1017, 2022
2022
Later among the works it cites.
G. Ji, J. Mun, H. Kim, and J. Hwangbo, “Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4630–4637, 2022
2022
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Cited alongside, same era.
W. Liu et al. , “Tlio: Tight learned inertial odometry,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5653–5660, 2020
2020
Cited alongside, same era.
M. Brossard, A. Barrau, and S. Bonnabel, “Ai-imu dead-reckoning,” IEEE Transactions on Intelligent Vehicles , vol. 5, no. 4, pp. 585–595, 2020
2020
Cited alongside, same era.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics , vol. 5, no. 47, p. eabc5986, 2020
2020
Cited alongside, same era.
2021
Cited alongside, same era.
J.-H. Kim et al. , “Legged robot state estimation with dynamic contact event information,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 6733–6740, 2021
2021
Cited alongside, same era.
M. Zhang, M. Zhang, Y. Chen, and M. Li, “Imu data processing for inertial aided navigation: A recurrent neural network based approach,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 3992–3998
2021
Cited alongside, same era.
S. Sun, D. Melamed, and K. Kitani, “Idol: Inertial deep orientation-estimation and localization,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 7, 2021, pp. 6128–6137
2021
Cited alongside, same era.
D. Wisth, M. Camurri, and M. Fallon, “Vilens: Visual, inertial, lidar, and leg odometry for all-terrain legged robots,” IEEE Transactions on Robotics , vol. 39, no. 1, pp. 309–326, 2022
2022
Later among the works it cites.
K. Zhang et al. , “Dido: Deep inertial quadrotor dynamical odometry,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 9083–9090, 2022
2022
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R. Buchanan, M. Camurri, F. Dellaert, and M. Fallon, “Learning inertial odometry for dynamic legged robot state estimation,” in Conference on robot learning . PMLR, 2022, pp. 1575–1584
2022
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2023
Later among the works it cites.
S. Choi et al. , “Learning quadrupedal locomotion on deformable terrain,” Science Robotics , vol. 8, no. 74, p. eade2256, 2023
2023
Later among the works it cites.
Y. Jin, W.-A. Zhang, H. Sun, and L. Yu, “Learning-aided inertial odometry with nonlinear state estimator on manifold,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Later among the works it cites.
G. Cioffi, L. Bauersfeld, E. Kaufmann, and D. Scaramuzza, “Learned inertial odometry for autonomous drone racing,” IEEE Robotics and Automation Letters , vol. 8, no. 5, pp. 2684–2691, 2023
2023
Later among the works it cites.