Intermittent measurement analysis for mobile robot application - Nurul Hasna, Hassan (2016)
This research is about the investigating of the condition during intermittent measurement when Extended Kalman Filter (EKF) is applied for Simultaneous Localization and Mapping (SLAM) problem of mobile robot during measuring and estimating its environment while updating its location consistently. This problem is being analyzed because to reduce the mobile robot uncertainties or covariance state during it is working also to prevent data loosing that to be updated to the monitor. There are two purposes of this research. Firstly is to determine the performance of Extended Kalman Filter based Simultaneous Localization and Mapping (SLAM) and secondly is to determine the condition during intermittent measurement when Extended Kalman Filter (EKF) is applied for Simultaneous Localization and Mapping (SLAM) problem. Simultaneous Localization and Mapping (SLAM) method is used in achieving the objective; to determine the condition of statistical bound during intermittent measurement. A few technical approaches such as Extended Kalman Filter (EKF), H infinity Filter, Unscented Kalman Filter (UKF) and Particle Filter are used for estimation purposes. EKF is the most recommended method for SLAM solution. This is because the filter offers simple algorithm to follow and has lower computational cost compared to others.