Covariance Bounds Analysis during Intermittent Measurement for EKF-based SLAM
This paper deals with Extended Kalman Filter(EKF)-based SLAM estimation considering intermittent measurement. The study proposes a method to ensure that a designer is able to secure the state covariance or system uncertainties even though the mobile robot sometimes losses its measurement data during its observations. EKF which is based on Bayesian, is chosen in this work for estimation purposes in determining mobile robot position and the environment conditions. In addition, as most of estimation techniques uses Bayesian method, our propose results is expected to provide a general estimation guidelines especially on the filter statistical behavior. The analysis started from the review of theoretical studies of EKF including the expected condition occurred when mobile robot do not received information from its sensors. Simulation results have consistently supports our theoretical analysis which determines that a designer can determine the state error covariance explicitly whenever measurement data is not available.
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