Collaborative concurrent mapping and localization
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/8575 |
| _version_ | 1811098063477932032 |
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| author | Fenwick, John William, 1977- |
| author2 | Michael E. Cleary. |
| author_facet | Michael E. Cleary. Fenwick, John William, 1977- |
| author_sort | Fenwick, John William, 1977- |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001. |
| first_indexed | 2024-09-23T17:09:03Z |
| format | Thesis |
| id | mit-1721.1/8575 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:09:03Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/85752019-04-12T20:42:29Z Collaborative concurrent mapping and localization Fenwick, John William, 1977- Michael E. Cleary. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001. Includes bibliographical references. Autonomous vehicles require the ability to build maps of an unknown environment while concurrently using these maps for navigation. Current algorithms for this concurrent mapping and localization (CML) problem have been implemented for single vehicles, but do not account for extra positional information available when multiple vehicles operate simultaneously. Multiple vehicles have the potential to map an environment more quickly and robustly than a single vehicle. This thesis presents a collaborative CML algorithm that merges sensor and navigation information from multiple autonomous vehicles. The algorithm presented is based on stochastic estimation and uses a feature-based approach to extract landmarks from the environment. The theoretical framework for the collaborative CML algorithm is presented, and a convergence theorem central to the cooperative CML problem is proved for the first time. This theorem quantifies the performance gains of collaboration, allowing for determination of the number of cooperating vehicles required to accomplish a task. A simulated implementation of the collaborative CML algorithm demonstrates substantial performance improvement over non-cooperative CML. by John William Fenwick. S.M. 2005-08-23T21:24:05Z 2005-08-23T21:24:05Z 2001 2001 Thesis http://hdl.handle.net/1721.1/8575 49223496 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 130 p. 7000951 bytes 7000708 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Fenwick, John William, 1977- Collaborative concurrent mapping and localization |
| title | Collaborative concurrent mapping and localization |
| title_full | Collaborative concurrent mapping and localization |
| title_fullStr | Collaborative concurrent mapping and localization |
| title_full_unstemmed | Collaborative concurrent mapping and localization |
| title_short | Collaborative concurrent mapping and localization |
| title_sort | collaborative concurrent mapping and localization |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/8575 |
| work_keys_str_mv | AT fenwickjohnwilliam1977 collaborativeconcurrentmappingandlocalization |