1. Practical Extensions to Vision- Based Monte Carlo Localization Methods for Robot Soccer Domain Kemal Kaplan, Buluç Çelik, Tekin Meriçli, Çetin Meriçli ve H. Levent Akın Boğaziçi University, 2005

2. Basic Monte Carlo Localization 1.Quantize Environment 2.Initialize beliefs 3.Update beliefs 4.Resample 5.Mutate particles

3. Vision Based MCL •Use information from vision to update particles

4. Robocup Field Setup

5. Considering Number of Percepts Seen •One landmark with 0.75 confidence vs 4 landmarks with 0.9 confidence 0.9 x 0.9 x 0.9 x 0.9 = 0.6561 < 0.75 !!! •Proposed correction

6. Using Inter-Percept Distance

7. Using Inter-Percept Distance (II)

8. Using Inter-Percept Distance (III)

9. Variable-Size Number of Particles

10. Dynamic Window Size

11. Results

12. Reults (II)

13. Questions?