Initial population size estimation for a Differential-Evolution-based global localization filter

The mobile robot global localization problem, which can be deﬁned as the search of the robot's coordinates in a known environment with no initial information, can be solved by diferent methods. The localization ﬁlter that is proposed here is an optimization method based on the Diﬀerential Evolution algorithm, which is a population-based evolutionary technique that evolves with the time to the true solution. One of the most important challenges of any population-based global localization system is the initialization problem. The initialization problem consists of obtaining an appropriate initial population size for each situation. This number must be large enough to ensure that the global localization algorithm converges to the true pose, and it must be the minimum value that ensures a robust convergence. It is easy to check the ﬁrst condition, but it is more diﬃcult to deal with the second one. In this paper, we propose a solution that automatically generates an initial population that is based on the environment size and the amount of information contained in the ﬁrst observation of the mobile robot. The eﬃciency of the proposed method and its capabilities have been tested in a simulated indoor environment.

Initial population size estimation for a Differential-Evolution-based global localization filter Articles