Incorporating neuro-fuzzy with extended Kalman filter for simultaneous localization and mapping. (26th September 2019)

• Record Type: Journal Article
• Authors: Do, Cong Hung; Lin, Huei-Yung
• Is Part Of: International journal of advanced robotic systems. Volume 16:Number 5(2019:Sep./Oct.)
• Publication Date: 2019-09-26
• Subjects: Extended Kalman filter -- simultaneous localization and mapping -- neuro-fuzzy assistance -- partial swarm optimization; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881419874645
• Languages: English
• ISSNs: 1729-8806

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.