GP-UKF: Unscented Kalman Filters with Gaussian Process prediction and observation models

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2007, Pages 1901-1907

  Ko, Jonathan ^a   Klein, Daniel J ^b   Fox, Dieter ^c   Haehnel, Dirk ^a  

^a University of Washington   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

^c INTEL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INTERNATIONAL CONFERENCES; OBSERVATION MODELS; TRACKING QUALITY;

BOOLEAN FUNCTIONS; COMPUTER NETWORKS; CONTROL THEORY; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); INTELLIGENT ROBOTS; INTELLIGENT SYSTEMS; KALMAN FILTERS; MODEL STRUCTURES; NONLINEAR SYSTEMS; ROBOTICS; STATE ESTIMATION; TRELLIS CODES; UNCERTAINTY ANALYSIS; WAVE FILTERS;

PARAMETER ESTIMATION;

EID: 51349131913     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2007.4399284     Document Type: Conference Paper

References (16)

1. P. Abbeel, A. Coates, M. Montemerlo, A. Ng, and S. Thrun. Discriminative training of Kalman filters. In Proc. of Robotics: Science and Systems, 2005.
2. Y. Bar-Shalom, X.-R. Li, and T. Kirubarajan. Estimation with Applications to Tracking and Navigation. John Wiley, 2001.
3. B. Ferris, D. Hähnel, and D. Fox. Gaussian processes for signal strength-based location estimation. In Proc. of Robotics: Science and Systems, 2006.
4. A. Girard, C. Rasmussen, J. Quin̈onero Candela, and R. Murray-Smith. Gaussian process priors with uncertain inputs - application to multiple-step ahead time series forecasting. In Advances in Neural Information Processing Systems 15 (NIPS). 2005.
5. D. Grimes, R. Chalodhorn, and R. Rao. Dynamic imitation in a humanoid robot through nonparametric probabilistic inference. In Proc. of Robotics: Science and Systems, 2006.
6. S.J. Julier and J.K. Uhlmann. A new extension of the Kalman filter to nonlinear systems. In Proc. of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, 1997.
7. J. Ko, D.J. Klein, D. Fox, and D. Hähnel. Gaussian processes and reinforcement learning for identification and control of an autonomous blimp. In Proc. of the IEEE International Conference on Robotics & Automation (ICRA), 2007.
8. N.D. Lawrence. http://www.dcs.shef.ac.uk/-neil/fgplvm/.
9. B. Limketkai, D. Fox, and L. Liao. CRF-filters: Discriminative particle filters for sequential state estimation. In Proc. of the IEEE International Conference on Robotics & Automation (ICRA), 2007.
10. K. Liu, A. Hertzmann, and Z. Popovic. Learning physics-based motion style with nonlinear inverse optimization. In ACM Transactions on Graphics (Proc. of SIGGRAPH), 2005.
11. C. Plagemann, D. Fox, and W. Burgard. Efficient failure detection on mobile robots using Gaussian process proposals. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 2007.
12. V. Quoc, A. Smola, and S. Canu. Heteroscedastic Gaussian process regression. In Proc. of the International Conference on Machine Learning (ICML), 2005.
13. A. Raj, A. Subramanya, D. Fox, and J. Bilmes. Rao-Blackwellized particle filters for recognizing activities and spatial context from wearable sensors. In Experimental Robotics: The 10th International Symposium, Springer Tracts in Advanced Robotics (STAR). Springer Verlag, 2006.
14. C.E. Rasmussen and C.K.I. Williams. Gaussian processes for machine learning. The MIT Press, 2005.
15. E. Solak, R. Murray-Smith, W. Leithead, D. Leith, and C. Rasmussen. Derivative observations in Gaussian process models of dynamic systems. In Advances in Neural Information Processing Systems 15 (NIPS), 2002.
16. S. Thrun, W. Burgard, and D. Fox. Probabilistic Robotics. MIT Press, Cambridge, MA, September 2005. ISBN 0-262-20162-3.