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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...
Premature convergence often happens when a Monte Carlo localization (MCL) algorithm tries to localize a robot under highly symmetrical environments. In this paper, we propose a novel method of solving such problem for global localization by incorporating a multi-objective evolutionary approach to resample particles with two objectives, including particle weights and population distribution. By employing...
In this paper, we propose a new approach to solve the premature convergence problem in Monte Carlo Localization for global localization, entitled "a mechanism for preventing premature convergence". The algorithm uses a referenced relative vector to rearrange weight for each sample, allowing better exploration of a symmetrical environment, resulting in preventing premature convergence problem...
This paper proposes the use of Particle Swarm Optimization (PSO) to work with an Enhanced-ICP to effectively filter out outliers and avoid false matching points during the map building of an unknown environment, where PSO is used to solve the local optima problem to obtain better transformation results for two data sets with excessive difference in initial position and direction. Then, we use part...
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