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

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

Vision Based MCL •Use information from vision to update particles

Robocup Field Setup

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 = < 0.75 !!! •Proposed correction

Using Inter-Percept Distance

Using Inter-Percept Distance (II)

Using Inter-Percept Distance (III)

Variable-Size Number of Particles

Dynamic Window Size

Results

Reults (II)

Questions?