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Nonlinear pose (\textit{i.e,} attitude and position) filters arecharacterized with simpler structure and better tracking performance incomparison with other methods of pose estimation. A critical factor whendesigning a nonlinear pose filter is the selection of the error function.Conventional design of nonlinear pose filter design trade-off between fastadaptation and robustness. This paper introduces a new practical approach basedon fuzzy rules for on-line continuous tuning of the nonlinear pose filter. Eachof input and output membership functions are optimally tuned using graphicalsearch algorithm optimization considering both pose error and its rate ofchange. The proposed approach is characterized with high adaptation featuresand strong level of robustness. Therefore, the proposed approach results ofrobust and fast convergence properties. The simulation results show theeffectiveness of the proposed approach considering uncertain measurements andlarge error in initialization.

FLC tuned with Gravitational Search Algorithm for Nonlinear Pose Filter