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.

On the potential contributions of hybrid intelligent approaches to Multicomponent Robotic System development

“…Kalman Filters have been applied with some success to this problem [6,8,22,26,37,67,69,77,114] in the context of SLAM. There are already some hybridizations of Kalman Filter based approaches with fuzzy systems for single mobile robots in environments that do not fit in the linear modeling paradigm easily [27,34,69,101,117,119,120,131]. Hybridization of the Kalman Filter approaches with Bayesian, fuzzy or neural network approaches may lead to interesting HIS avenues for research in the framework of MCRS.…”

Zonotopic Linear Parameter Varying SLAM Applied to Autonomous Vehicles

“…In terms of addressing the inherent linearisation problem in most of the SLAM algorithms, we can see that there have been attempts at solving it with similar techniques to the LPV approach proposed in this work; for instance, Guerra et al [ 15 ] applied a similar approach to the nonlinear kinematic model of a tricycle robot; in another line of work, Pathiranage et al [ 16 ] used fuzzy logic to address the nonlinearity of the sensor models. On the other hand, we can see how model switching can also be used to enhance the performance of the system when facing variable noise conditions, as can be seen in [ 17 ].…”

Autonomous Vehicle State Estimation using a LPV Kalman Filter and SLAM

“…Then, the rough pose obtained from the SLAM algorithm, direct measurements from sensors will be corrected using the LPV Kalman filter design. A similar work [5] is done to improve the approximation error by utilizing Takagi-Sugeno model which is analogous to LPV form. However, this approach considers a limited and known number of landmarks and neither ensures optimality.…”