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Extended Kalman Filter

Day 25. Extended Kalman Filter. with slides adapted from http://www.probabilistic-robotics.com. Kalman Filter Summary. Highly efficient : Polynomial in measurement dimensionality k and state dimensionality n : O(k 2.376 + n 2 ) Optimal for linear Gaussian systems !

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Extended Kalman Filter

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  1. Day 25 Extended Kalman Filter with slides adapted from http://www.probabilistic-robotics.com

  2. Kalman Filter Summary • Highly efficient: Polynomial in measurement dimensionality k and state dimensionality n: O(k2.376 + n2) • Optimal for linear Gaussian systems! • Most robotics systems are nonlinear!

  3. Landmark Measurements • distance, bearing, and correspondence

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

  5. Nonlinear Dynamic Systems • localization with landmarks

  6. Linearity Assumption Revisited Figure 3.3 (a), p 55

  7. Non-linear Function Figure 3.3 (b), p 55

  8. EKF Linearization (1) Figure 3.4, p 57

  9. EKF Linearization (2) Figure 3.5 (a), p 62

  10. EKF Linearization (3) Figure 3.5 (b), p 62

  11. Taylor Series • recall for f(x) infinitely differentiable around in a neighborhood a • in the multidimensional case, we need the matrix of first partial derivatives (the Jacobian matrix)

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

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

  14. Localization “Using sensory information to locate the robot in its environment is the most fundamental problem to providing a mobile robot with autonomous capabilities.” [Cox ’91] • Given • Map of the environment. • Sequence of sensor measurements. • Wanted • Estimate of the robot’s position. • Problem classes • Position tracking • Global localization • Kidnapped robot problem (recovery)

  15. Landmark-based Localization

  16. EKF_localization( mt-1,St-1, ut, zt,m):Prediction: Jacobian of g w.r.t location Jacobian of g w.r.t control Motion noise Predicted mean Predicted covariance

  17. EKF_localization ( mt-1,St-1, ut, zt,m):Correction: Predicted measurement mean Jacobian of h w.r.t location Pred. measurement covariance Kalman gain Updated mean Updated covariance

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