One
of the most demanding skills for a mobile robot is to be intelligent
enough to know its own location. The global localization problem
consists of obtaining the robot’s pose (position and orientation) in a
known map if the initial location is unknown. This task is addressed
applying evolutionary computation concepts (Differential Evolution). In
the current approach, the distances obtained from the laser sensors are
combined with the predicted scan (in the known map) from possible
locations to implement a cost function that is optimized by an
evolutionary filter. The laser beams (sensor information) are modeled
using a combination of probability distributions to implement a
nonsymmetric fitness function. The main contribution of this work is to
apply the probabilistic approach to design three different cost
functions based on known divergences (Jensen-Shannon, Itakura- Saito,
and Density Power). The three metrics have been tested in different
experiments and the localization module performance is exceptional in
regions with occlusions caused by different obstacles. This fact
validates that the non-symmetric probabilistic approach is a suitable
technique to be applied to multiple metrics.