Monte carlo localization in highly symmetric environments

Abstract

The localization problem is a central issue in mobile robotics. Monte Carlo Localization (MCL) is a popular method to solve the localization problem for mobile robots. However, usual MCL has some shortcomings in terms of computational complexity, robustness and the handling of highly symmetric environments. These three issues are adressed in this work. We present three Monte Carlo localization algorithms as a solution to these problems. The focus lies on two of these, which are especially suitable for highly symmetric environments, for which we introduce two-stage sampling as the resampling scheme.