One of the most important skills desired for a mobile robot is the ability to
obtain its own location even in challenging environments. The information provided by
the sensing system is used here to solve the global localization problem. In our previous
work, we designed different algorithms founded on evolutionary strategies in order to solve
the aforementioned task. The latest developments are presented in this paper. The engine
of the localization module is a combination of the Markov chain Monte Carlo sampling
technique and the differential evolution method, which results in a particle filter based
on the minimization of a fitness function. The robot’s pose is estimated from a set of
possible locations weighted by a cost value. The measurements of the perceptive sensors
are used together with the predicted ones in a known map to define a cost function to
optimize. Although most localization methods rely on quadratic fitness functions, the sensed
information is processed asymmetrically in this filter. The Kullback-Leibler divergence is
the basis of a cost function that makes it possible to deal with different types of occlusions.
The algorithm performance has been checked in a real map. The results are excellent in
environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the
sensing area.