Abstract
This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using pseudolinear process and measurement models. Nonlinear process model and observation model are formulated as pseudo-linear models and rewritten with a composite model whose local models are linear according to Takagi-Sugeno (T-S) fuzzy model. Using the Kalman filter theory, each local T-S model is filtered to find the local estimates. The linear combination of these local estimates gives the global estimate for the complete system. Data association to correspond features to the observed measurement is proposed with two sensor frames obtained from two sensors. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
