New solutions to the premature convergence problem in Monte Carlo localization

Abstract

In this paper, a new solution towards the premature convergence problem in Monte Carlo Localization for global localization under highly symmetrical environments is proposed. The algorithm employs a “standard direction” to allow particles to move so as to rearrange weights, providing better exploration as a result. Therefore, there are higher opportunities for particles to converge to the real robot pose and prevent premature convergence accordingly. Experiments have verified the proposed algorithm to be reliable and robust by offering notable improvements in the global localization performance.