Practical Extensions to Vision-Based Monte Carlo Localization Methods for Robot Soccer Domain

This paper proposes a set of practical extensions to the vision-based Monte Carlo localization for RoboCup Sony AIBO legged robot soccer domain. The main disadvantage of AIBO robots is that they have a narrow field of view so the number of landmarks seen in one frame is usually not enough for geometric calculation. MCL methods have been shown to be accurate and robust in legged robot soccer domain but there are some practical issues that should be handled in order to maintain stability/elasticity ratio in a reasonable level. In other words, the fast convergence ability is required in case of kidnapping. But on the other hand, fast converge can be vulnerable when an occasional bad sensor reading is received. In this work, we presented four practical extensions in which two of them are novel approaches and the remaining ones are different from the previous implementations.