Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Self-adaptive Monte Carlo localization for mobile robots using range sensors
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Omnivision-based KLD-Monte Carlo Localization
Robotics and Autonomous Systems
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
L1-L2-norm comparison in global localization of mobile robots
Robotics and Autonomous Systems
A global localization approach based on Line-segment Relation Matching technique
Robotics and Autonomous Systems
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
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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.