Abstract
Extended Kalman filter is well-known as a popular solution to the simultaneous localization and mapping problem for mobile robot platforms or vehicles. In this article, the development of a neuro-fuzzy-based adaptive extended Kalman filter technique is presented. The objective is to estimate the proper values of the R matrix at each step. We design an adaptive neuro-fuzzy extended Kalman filter to minimize the difference between the actual and theoretical covariance matrices of the innovation consequence. The parameters of the adaptive neuro-fuzzy extended Kalman filter is then trained offline using a particle swarm optimization technique. With this approach, the advantages of high-dimensional search space can be exploited and more effective training can be achieved. In the experiments, the mobile robot navigation with a number of landmarks under two benchmark situations is evaluated. The results have demonstrated that the proposed adaptive neuro-fuzzy extended Kalman filter technique provides the improvement in both performance efficiency and computational cost.
Highlights
The problem of simultaneous localization and mapping (SLAM) has been investigated for many decades, and it is still a challenging issue for mobile robotics research till
In the conventional extended Kalman filter (EKF)-based SLAM, the matrices Qk and Rk are fixed throughout the experiments, while in the adaptive neuro-fuzzy EKF (ANFEKF)-based SLAM, Qk is constant but Rk is adjusted by the adaptive neuro-fuzzy inference system (ANFIS) system
This work proposed an ANFIS for SLAM based on EKF
Summary
The problem of simultaneous localization and mapping (SLAM) has been investigated for many decades, and it is still a challenging issue for mobile robotics research till now. Inconsistent feature observations or noisy sensor readings can both lead to wrong inferences of the SLAM system To cope with these problems, this work investigates the integration of both the absolute and relative methods based on the extended Kalman filter (EKF).[4,5] The EKF is wellknown as a method for multi-sensor fusion. One effective solution to deal with this issue is to employ adaptive algorithms for EKF SLAM.[9,10,11,12,13] the studies of the literature[14,15] have shown that the approach of artificial intelligence-assisted EKF for the SLAM problems is far more superior than the conventional approaches (e.g. unscented filter, square root unscented filter) and others (e.g. particle filter) Their conclusions and results have greatly motivated this work for a further investigation. A few work have addressed some of the above issues, none of them offers all three layers of solutions presented in this article
Sign-in/Register to view highlights & summary 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.
