Optimal State Estimation , Sensor Fusion Study - Ch14. The Unscented Kalman Filter

1. Unscented Kalman Filter
Example Based

2. Content
● Simple Conception of UKF
● Example Based UKF
● Real World Example

3. Handling
Linear / Non-Linear
Transformation
theta….???

4. EKF - Linearization, Jacobian

5. EKF의 단점
● Linearized Mean/Cov != Nonlinear Mean/Cov
● Jacobian 계산이 너무 오래 걸림 - Stink!!
● 너무 복잡한 model, 미분할 수 없는 수식이 들어간 모델에
취약

6. Stink!

7. Unscented Kalman Filter
and Sigma Points

8. ● How pick points
● How many points
● After then...

9. Example Based UKF

10. https://www.tesla.com/autopilot

11. Example - CTRV Model

12. Generate Sigma Points - 2D

13. Generate Sigma Points
- CTRV Model

14. Generate Sigma Points
Effect of Design parameter

15. Various Form...

16. Take account process noise
Kalman Filter에서는…
http://campar.in.tum.de/Chair/KalmanFilter

17. Augmentation

18. Sigma Points with Augmentation

19. Predict Sigma Points
Based on State Model
http://campar.in.tum.de/Chair/KalmanFilter
Example...

20. Predict Mean and Covariance

21. Recap

22. Predict Measurement
5 state
=> 7 * 15 matrix (augmented)
=> 5 * 15 matrix & new mean/cov (predict)
=> 3 * 15 (projection to measurement space)
=> calc mean & cov again

23. Update State

24. Process Noise…???
● The process noise is usually treated as a tuning parameter to adjust
the gain of the Kalman filter to smooth either more or less the data.
● want to respond quickly to changes. => Upper Process Noise

25. Real World Example

26. EKF/UKF in Localization
https://github.com/kimsooyoung/AI_KR_KF_Examples

27. Odometry & Sensor Fusion
https://github.com/udacity/robot_pose_ekf
https://www.researchgate.net/figure/Paramet
● robot_pose_ekf => deprecated
● robot_localization

28. Q & A

29. Reference
● The UKF exposed: How it works, when it works and when
it's better to sample
● Udacity - Sensor Fusion Nanodegree
● cra-ros-pkg/robot_localization

30. Thank you :)