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This paper develops a new nonlinear filter, called Moment-based Kalman Filter (MKF), using the exact moment propagation method.
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S. Julier and J. Uhlmann, “A new extension of the Kalman filter to nonlinear systems,” in Proc. of AeroSense: The 11th Int. Symp. on Aerospace/Defense Sensing, Simulations and Controls , 1997
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E. Wan and R. Van Der Merwe, “The unscented Kalman filter for nonlinear estimation,” in Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373) , 2000, pp. 153–158
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R. Merwe and E. Wan, “The square-root unscented Kalman filter for state and parameter-estimation,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing , vol. 6, 02 2001, pp. 3461 – 3464 vol.6
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B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman filter: Particle filters for tracking applications . Artech house, 2003
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S. Julier and J. Uhlmann, “Unscented filtering and nonlinear estimation,” Proceedings of the IEEE , vol. 92, no. 3, pp. 401–422, 2004
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S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics . The MIT Press, 2005
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D. Simon, Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches . USA: Wiley-Interscience, 2006
2006
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Q. SONG and J.-D. HAN, “An adaptive UKF algorithm for the state and parameter estimations of a mobile robot,” Acta Automatica Sinica , vol. 34, no. 1, pp. 72–79, 2008. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1874102908600026
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G. G. Rigatos, “Particle filtering for state estimation in industrial robotic systems,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering , vol. 222, no. 6, pp. 437–455, 2008. [Online]. Available: https://doi.org/10.1243/09596518JSCE463
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S. Bonnabel, P. Martin, and P. Rouchon, “Symmetry-preserving observers,” IEEE Transactions on Automatic Control , vol. 53, no. 11, pp. 2514–2526, 2008
2008
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2018
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T. Kim and T.-H. Park, “Extended Kalman filter (EKF) design for vehicle position tracking using reliability function of Radar and LIDAR,” Sensors , vol. 20, no. 15, 2020. [Online]. Available: https://www.mdpi.com/1424-8220/20/15/4126
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A. Wang, X. Huang, A. Jasour, and B. Williams, “Fast risk assessment for autonomous vehicles using learned models of agent futures,” in Proceedings of the Robotics: Science and Systems Conference , 07 2020
2020
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R. Hartley, M. Ghaffari, R. M. Eustice, and J. W. Grizzle, “Contact-aided invariant extended Kalman filtering for robot state estimation,” International Journal of Robotics Research , vol. 39, no. 4, pp. 402–430, 2020
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J. van den Berg, P. Abbeel, and K. Goldberg, “LQG-MP: optimized path planning for robots with motion uncertainty and imperfect state information,” International Journal of Robotics Research , vol. 30, no. 7, pp. 895–913, 2011. [Online]. Available: https://doi.org/10.1177/0278364911406562
2011
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E. Cinlar, Probability and Stochastics , ser. Graduate Texts in Mathematics. Springer New York, NY, 2011
2011
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K. Y. Leung, Y. Halpern, T. D. Barfoot, and H. H. Liu, “The utias multi-robot cooperative localization and mapping dataset,” International Journal of Robotics Research , vol. 30, no. 8, pp. 969–974, 2011. [Online]. Available: https://doi.org/10.1177/0278364911398404
2011
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R. D. Turner, “Gaussian processes for state space models and change point detection,” 2012
2012
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A. Lee, Y. Duan, S. Patil, J. Schulman, Z. McCarthy, J. van den Berg, K. Goldberg, and P. Abbeel, “Sigma hulls for gaussian belief space planning for imprecise articulated robots amid obstacles,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , 2013, pp. 5660–5667
2013
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K. György, A. Kelemen, and L. Dávid, “Unscented Kalman filters and particle filter methods for nonlinear state estimation,” Procedia Technology , vol. 12, p. 65–74, 12 2014
2014
Cited alongside, same era.
P. Chen, H. Ma, S. Gao, and Y. Huang, “Modified extended Kalman filtering for tracking with insufficient and intermittent observations,” Mathematical Problems in Engineering , vol. 2015, pp. 1–9, 09 2015
2015
Cited alongside, same era.
S. Yang and M. Baum, “Extended Kalman filter for extended object tracking,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing , 2017, pp. 4386–4390
2017
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2020
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J. Chen, Y. Shimizu, L. Sun, M. Tomizuka, and W. Zhan, “Constrained iterative lqg for real-time chance-constrained gaussian belief space planning,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE Press, 2021, p. 5801–5808. [Online]. Available: https://doi.org/10.1109/IROS51168.2021.9636187
2021
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2021
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A. Jasour, X. Huang, A. Wang, and B. Williams, “Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures,” in Autonomous Robots , vol. 46, 2021, pp. 269–282
2021
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R. Mahony and J. Trumpf, “Equivariant filter design for kinematic systems on Lie groups,” IFAC-PapersOnLine , vol. 54, no. 9, pp. 253–260, 2021
2021
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W. Han, A. Jasour, and B. Williams, “Non-gaussian risk bounded trajectory optimization for stochastic nonlinear systems in uncertain environments,” in IEEE International Conference on Robotics and Automation (ICRA) , 2022
2022
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S. Teng, D. Chen, W. Clark, and M. Ghaffari, “An error-state model predictive control on connected matrix Lie groups for legged robot control,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , 2022, pp. 8850–8857
2022
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S. Teng, W. Clark, A. Bloch, R. Vasudevan, and M. Ghaffari, “Lie algebraic cost function design for control on Lie groups,” in Proceedings of the IEEE Conference on Decision and Control , 2022, pp. 1867–1874
2022
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M. Ghaffari, R. Zhang, M. Zhu, C. E. Lin, T.-Y. Lin, S. Teng, T. Li, T. Liu, and J. Song, “Progress in symmetry preserving robot perception and control through geometry and learning,” Frontiers in Robotics and AI , vol. 9, p. 232, 2022
2022
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