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 Environment2. Initialize beliefs3. Update beliefs4. Resample5. 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 = 0.6561 < 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?