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This paper describes a Takagi-Sugeno (T-S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Fuzzy Kalman filtering of the SLAM problem (FKF-SLAM) is used in this paper together with newly proposed data association algorithm. An extended TSDA (ETSDA) method is introduced for the SLAM problem in mobile robot navigation...
This paper describes an improved solution to the simultaneous localization and mapping (SLAM) problem based on pseudolinear models. Accurate estimation of vehicle and landmark states is one of the key issues for successful mobile robot navigation if the configuration of the environment and initial robot location are unknown. A state estimator which can be designed to use the nonlinearity as it is...
This paper describes an improved solution to mobile robot localization and map building problem based on pseudolinear measurement model through bias reduction approach. Accurate estimation of vehicle and landmark states is one of the key issues for successful mobile robot navigation if the configuration of the environment and initial robot location are unknown. Direct linearization of nonlinear models...
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