Kullback–Leibler divergence-based global localization for mobile robots

Abstract

The global localization problem for mobile robots is addressed in this paper. In this field, the most common approaches solve this problem based on the minimization of a quadratic loss function or the maximization of a probability distribution. The distances obtained from the perceptive sensors are used together with the predicted ones (from the estimates in the known map) to define a cost function or a probability to optimize. In our previous work, we developed an optimization-based global localization module that used evolutionary computation concepts. In particular, the algorithm engine was the Differential Evolution method. In this work, this algorithm has been modified including the minimization of the Kullback–Leibler divergence between true observations and estimates. This divergence is used to calculate the cost function of the localization module. The algorithm has been tested in different situations and the most important improvement is the ability to cope with different types of occlusions.