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

Sensors

Fernando Martín¹  Luis Moreno²  Santiago Garrido²  Dolores Blanco² 

[1] Robotics Lab, Carlos III University, Madrid 28911, Spain;

关键词: Markov chain Monte Carlo;    Kullback-Leibler divergence;    differential evolution;    mobile robot;    global localization;    laser range finders;   

DOI  :  10.3390/s150923431

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.