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About the Project The Week of Nov 3 Two iRobot/Roomba papers

About the Project The Week of Nov 3 Two iRobot/Roomba papers. Nonlinear Dynamic Systems. Most realistic robotic problems involve nonlinear functions. Linearity Assumption Revisited. See figure 3.3-3.6. Non-linear Function. EKF Linearization (1). EKF Linearization (2). EKF Linearization (3).

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About the Project The Week of Nov 3 Two iRobot/Roomba papers

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  1. About the ProjectThe Week of Nov 3Two iRobot/Roomba papers

  2. Nonlinear Dynamic Systems • Most realistic robotic problems involve nonlinear functions

  3. Linearity Assumption Revisited See figure 3.3-3.6

  4. Non-linear Function

  5. EKF Linearization (1)

  6. EKF Linearization (2)

  7. EKF Linearization (3)

  8. EKF Linearization: First Order Taylor Series Expansion • Prediction: • Correction:

  9. EKF Algorithm • Extended_Kalman_filter( mt-1,St-1, ut, zt): • Prediction: • Correction: • Returnmt,St

  10. Bayes Filter Implementations (2) Particle filters

  11. Particle Filters • Represent belief by random samples • Estimation of non-Gaussian, nonlinear processes • Monte Carlo filter, Survival of the fittest, Condensation, Bootstrap filter, Particle filter • Filtering: [Rubin, 88], [Gordon et al., 93], [Kitagawa 96] • Computer vision: [Isard and Blake 96, 98] • Dynamic Bayesian Networks: [Kanazawa et al., 95]

  12. Sample-based Localization (sonar)

  13. Particle Filters

  14. Sensor Information: Importance Sampling

  15. Robot Motion

  16. Particle Filter Algorithm http://www.site.uottawa.ca/research/spot/index_fichiers/Theory%20and%20Implementation%20of%20Particle%20Filters.ppt#307,31,Resampling

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