practical extensions to vision- based monte carlo localization methods for robot soccer domain kemal...
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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?