A solution to the SLAM problem based on fuzzy Kalman filter using pseudolinear measurement model

This paper proposes a fuzzy logic based solution to the SLAM problem. Less error prone vehicle process model is proposed to improve the accuracy and the faster convergence of state estimation. Evolution of vehicle motion is modeled using dead-reckoned odometry measurements as control inputs. Nonlinear process model and observation model are formulated as pseudolinear models and approximated by local linear models according to the T-S fuzzy model. Linear Kalman filter equations are then used to estimate the state of the approximated local linear models. Combination of these local state estimates results in global state estimate. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem. The proposed method shows a way to use nonlinear systems in Kalman filter estimator without using Jacobian matrices. It is found that a fuzzy logic based approach with the pseudolinear models provides a demanding solution to state estimation.