Robust mobile robot localization using a evolutionary particle filter

Authors:
Bo Yin;Zhiqiang Wei;Xiaodong Zhuang
Affiliations:
Computer Science Department of Ocean University of China, Qingdao, China;Computer Science Department of Ocean University of China, Qingdao, China;Computer Science Department of Ocean University of China, Qingdao, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Robust Monte Carlo localization for mobile robots

Artificial Intelligence

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Abstract

The application of the auxiliary particle filter to the robot localization problem is considered. The auxiliary particle filter (APF) is an enhancement of the generic particle filter. However, APF suffers from the impoverishment problem and needs a large number of particles to represent the system posterior probability density function. An evolutionary computing method, the genetic algorithm is introduced into APF to remove early convergence yet improves the quality of potential solutions. Experiment results show that the evolutionary APF algorithm needs fewer particles and is more precise and robust for mobile robot localization in dynamic environment.