A Solution to Partial Observability in Extended Kalman Filter Mobile Robot Navigation

Abstract

Partial ob servab ility in EKF b ased mob ile rob ot navigation is investigated in this paper to find a solution that can prevent erroneous estimation. By only considering certain landmarks in an environment, the computational cost in mob ile robot can b e reduced b ut with an increase of uncertainties to the system. This is known as sub optimal condition of the system. Fuzzy Logic technique is proposed to ensure that the estimation achieved desired performance even though some of the landmarks were excluded for references. The Fuzzy Logic is applied to the measurement innovation of Kalman Filter to correct the positions of b oth mob ile rob ot and any ob served landmarks during ob servations. The simulation results shown that the proposed method is capab le to secure reliab le estimation results even a numb er of landmarks b eing excluded from Kalman Filter update process in b oth Gaussian and non-Gaussian noise conditions.

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

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