Abstract
Because the problem of the extended Kalman filter localization and mapping algorithm priori noise model is difficult to manage, this paper proposes an improved wild geese particle swarm algorithm based on the fuzzy adaptive Kalman filter localization and mapping algorithm. We take advantage of the the fractional calculus to improve particle speed of evolution, and make use of chaos to improve the initialization of the particle and the precocious one when processing. The improvement of wild geese particle swarm algorithm is shown in convergence rate and avoiding premature, then they can improve geese particle swarm algorithm for fuzzy adaptive extended Kalman filter localization and mapping algorithm training. in contrast with geese particle swarm algorithm fuzzy adaptive extended Kalman filter simultaneous localization and mapping algorithm, the new algorithm positioning and composition has greatly improved.
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