Measurement noise estimator assisted extended kalman filter for SLAM problem

Authors:
Won-Seok Choi;Jeong-Gwan Kang;Se-Young Oh
Affiliations:
Department of Electronic and Electrical Engineering, the POhang university of Science and TECHnology, Pohang, Korea;Department of Electronic and Electrical Engineering, the POhang university of Science and TECHnology, Pohang, Korea;Department of Electronic and Electrical Engineering, the POhang university of Science and TECHnology, Pohang, Korea
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

This paper addresses the measurement noise of Extended Kalman Filter-based Simultaneous Localization And Mapping (EKF-SLAM). The Extended Kalman Filter (EKF) is based on the Gaussian noise with zero mean and should know the correct prior knowledge of control and measurement noise covariance matrices. If these conditions are not satisfied, EKF unavoidably diverges. The present paper proposes the method of a new adaptive kalman filter to be supported by Measurement Noise Estimator (MNE), which estimates the measurement noise distribution including biased noise and noise covariance, whenever the update step executes. We evaluate this method under well-known benchmark environment for SLAM problem. Simulation results show that the proposed algorithm overcomes degrading performance of the standard EKF under the condition of wrong knowledge of sensor statistics.