FEKF Estimation for Mobile Robot Localization and Mapping Considering Noise Divergence

摘要

This paper proposed an approach of Fuzzy-Extended Kalman Filter(FEKF) for mobile robot localization and mapping under unknown noise characteristics. The technique apply the information extracted from EKF measurement innovation to derive the best estimation output for a mobile robot during its observations. These information is then fuzzified using Fuzzy Logic technique with very few design rules to control the information which at the end further reducing the error about the measurement and consequently provide better localization and mapping. Simulation results are also presented to describe the efficiency of the proposed method in comparison with the normal EKF estimation that emphasize FEKF exceeds the estimation results of normal EKF in non-Gaussian noise environment.