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Understanding Extended Kalman Filter for Robotic Localization

Learn about the Extended Kalman Filter (EKF) for nonlinear dynamic systems in robotics. Dive into EKF linearization, prediction, correction steps, and its application in landmark-based localization. Explore how EKF aids in tracking a robot's position in its environment.

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Understanding Extended Kalman Filter for Robotic Localization

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  1. 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

  7. Non-linear Function

  8. EKF Linearization (1)

  9. EKF Linearization (2)

  10. EKF Linearization (3)

  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. Revisit omnibot example

  17. 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

  18. 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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