Abstract:The two main challenges offered by Simultaneous Localization and Mapping (SLAM) are that of observability and extending state estimation to exploration. This thesis explores and uses solutions to render the SLAM problem observable, by proposing the Reconfigurable Extended Kalman Filter (EKF) that addresses imposing observability, maintaining observability and choice of observability constraints. Additionally, Bayesian theory and Dempster-Shafer theory of evidential reasoning are analyzed, and Occupancy grid ba… Show more
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