Sparse Bayesian information filters for localization and mapping
Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2008.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2009
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| Online Access: | http://hdl.handle.net/1721.1/46498 |
| _version_ | 1811091158186590208 |
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| author | Walter, Matthew R |
| author2 | John J. Leonard. |
| author_facet | John J. Leonard. Walter, Matthew R |
| author_sort | Walter, Matthew R |
| collection | MIT |
| description | Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2008. |
| first_indexed | 2024-09-23T14:57:55Z |
| format | Thesis |
| id | mit-1721.1/46498 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:57:55Z |
| publishDate | 2009 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/464982019-04-11T10:31:05Z Sparse Bayesian information filters for localization and mapping Walter, Matthew R John J. Leonard. Woods Hole Oceanographic Institution. Joint Program in Oceanography/Applied Ocean Science and Engineering. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Woods Hole Oceanographic Institution. /Woods Hole Oceanographic Institution. Joint Program in Oceanography/Applied Ocean Science and Engineering. Mechanical Engineering. Woods Hole Oceanographic Institution. Vehicles, Remotely piloted Computer simulation Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2008. Includes bibliographical references (p. 159-170). This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment.We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. (cont) This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKEF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull. by Matthew R. Walter. S.M. 2009-08-26T16:36:55Z 2009-08-26T16:36:55Z 2008 2008 Thesis http://hdl.handle.net/1721.1/46498 401745224 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 170 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | /Woods Hole Oceanographic Institution. Joint Program in Oceanography/Applied Ocean Science and Engineering. Mechanical Engineering. Woods Hole Oceanographic Institution. Vehicles, Remotely piloted Computer simulation Walter, Matthew R Sparse Bayesian information filters for localization and mapping |
| title | Sparse Bayesian information filters for localization and mapping |
| title_full | Sparse Bayesian information filters for localization and mapping |
| title_fullStr | Sparse Bayesian information filters for localization and mapping |
| title_full_unstemmed | Sparse Bayesian information filters for localization and mapping |
| title_short | Sparse Bayesian information filters for localization and mapping |
| title_sort | sparse bayesian information filters for localization and mapping |
| topic | /Woods Hole Oceanographic Institution. Joint Program in Oceanography/Applied Ocean Science and Engineering. Mechanical Engineering. Woods Hole Oceanographic Institution. Vehicles, Remotely piloted Computer simulation |
| url | http://hdl.handle.net/1721.1/46498 |
| work_keys_str_mv | AT waltermatthewr sparsebayesianinformationfiltersforlocalizationandmapping |