Observable 2D SLAM and Evidential Occupancy Grids
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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 based maps based on Dempster-Shafer theory of evidential reasoning are created and analyzed in large environment for their potential use in exploration and obstacle avoidance. Tackling both issues with different algorithms yield better solutions to the challenges offered by robotic exploration, and this is demonstrated through simulation results in representative environments.
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Copyright © 2014 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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radhakrishnan-observable2dslamandevidentialoccupancygrids.pdf | 2023-05-04 | Public | Download |