1. Robust Monte Carlo Localization for Mobile Robots
Thomas Coffee
Based on:
Thrun S, Fox D, Burgard W, Dellaert F
Robust Monte Carlo Localization for Mobile Robots (2001)
Artificial Intelligence 128(1-2):
Image: Thrun et al. 2001

2. The Problem of 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, 1991)
Image: Fox et al. 1999

3. Tracking vs. Global Localization

4. Global Localization Requires Multi-Modal Belief Representations
Image: Fox et al. 1999

5. Global Localization for a Mobile Robot
Image: Thrun et al. 2001

6. Multi-Hypothesis Kalman Filtering
Image: Roumeliotis et al. 2000

7. Real Errors are Non-Gaussian!
Image: Thrun et al. 2001

8. Markov Localization (ML)
Image: Fox et al. 1999

9. Particle Filters to the Rescue!
Image: Thrun et al. 2001

10. Monte Carlo Localization (MCL)
Image: Thrun et al. 2001

11. Monte Carlo Localization (MCL)
Image: Thrun et al. 2001

12. Monte Carlo Localization (MCL)
Image: Thrun et al. 2001

13. How MCL Works

14. Performance of MCL vs. ML
Image: Thrun et al. 2001

15. Simulated Object Localization with MCL
Image: Thrun et al. 2001

16. Better Sensors = Larger Errors?
Image: Thrun et al. 2001

17. Object Localization Failure with MCL
Image: Thrun et al. 2001

18. What Went Wrong?

19. A Quick Fix for MCL
Image: Thrun et al. 2001

20. Key Idea: Dual Sampling MCL

21. Kernel Density Trees: Computing Densities from Particle Fields
Recursive sampling above threshold
Calculate densities by sum of weights in leaf divided by volume of leaf
Equivalent to maximum likelihood estimation of piecewise constant density functions
Like particle filters, concentrates effort in most useful regions
Image: Fox et al. 2000

22. Results of Dual MCL
Image: Thrun et al. 2001

23. Mixture-MCL: Best of Both Breeds
Image: Thrun et al. 2001

24. Results for Small Samples
Image: Thrun et al. 2001

25. Results for the Kidnapping Problem
Image: Thrun et al. 2001

26. Real Implementation of Mixture-MCL: Sampling Poses from Observations

27. Mixture-MCL in Action
Image: Thrun et al. 2001

28. Mixture-MCL in Action
Image: Thrun et al. 2001

29. Mixture-MCL in Action
Image: Thrun et al. 2001

30. Results for Real Implementation
Image: Thrun et al. 2001

31. Is Mixture-MCL Efficient?
Image: Thrun et al. 2001

32. Almost as Fast as Standard MCL!
Image: Thrun et al. 2001

33. Advantages of Mixture-MCL

34. Related Work and Applications

35. Limitations and Assumptions

36. Future Extensions to Mixture-MCL

37. Thank you!
Image: Thrun et al. 1999