A fuzzy logic based approach to the SLAM problem using pseudolinear models with two sensors data association

Abstract

This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using a pseudolinear measurement model of nonholonomic mobile robots. Takagi-Sugeno fuzzy model based on observation for nonlinear system is adopted to represent the process and measurement models of the vehicle-landmarks system. The complete system of the vehicle-landmarks model is decomposed into several linear models. Using the Kalman filter theory, each local model is filtered to find the local estimates. The linear combination of these local estimates gives the global estimate for the complete system. The simulation results prove that the new approach results in more anticipated performances, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach.