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Sensors 2015, 15(9), 23431-23458; doi:10.3390/s150923431

Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots

Robotics Lab, Carlos III University, Madrid 28911, Spain
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Author to whom correspondence should be addressed.
Academic Editor: Hong-Nan Li
Received: 15 July 2015 / Revised: 17 August 2015 / Accepted: 6 September 2015 / Published: 16 September 2015
(This article belongs to the Section Physical Sensors)
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Abstract

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. View Full-Text
Keywords: Markov chain Monte Carlo; Kullback-Leibler divergence; differential evolution; mobile robot; global localization; laser range finders Markov chain Monte Carlo; Kullback-Leibler divergence; differential evolution; mobile robot; global localization; laser range finders
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Martín, F.; Moreno, L.; Garrido, S.; Blanco, D. Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots. Sensors 2015, 15, 23431-23458.

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