Abstract
AbstractThe 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.KeywordsMobile RobotParticle FilterGenetic AlgoRobot LocalizationAverage Localization ErrorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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