Adaptive neuro fuzzy supported Kalman filter approach for simultaneous localization and mapping

Simultaneous Localization and Mapping (SLAM) is a method employed by robots and autonomous vehicles to build up a map within an unknown environment or to update a map within a known environment. In recent years, SLAM has been a significant problem with autonomous. There have been different statistical methods used for solving this problem ranging from expectation maximization method to Kalman based filters and particle filters. In this study, square root uncented Kalman filter has been utilized to address the SLAM problem. Two basic improvements have been achieved with the proposed method i) tuning Q and R design matrices using adaptive neuro fuzzy inference system (ANFIS), ii) Rauch-Tung-Striebel smoother for enhancing the filter´s prediction. Simulation results have shown that the proposed filter is more successful compared with the extended, unscented, square root uncented Kalman filters and particle based FASTSLAM II model.