Robust and efficient robotic mapping
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 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/1721.1/44713 |
| _version_ | 1826199453947658240 |
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| author | Olson, Edwin B. (Edwin Brock), 1977- |
| author2 | Seth Teller and John Leonard. |
| author_facet | Seth Teller and John Leonard. Olson, Edwin B. (Edwin Brock), 1977- |
| author_sort | Olson, Edwin B. (Edwin Brock), 1977- |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. |
| first_indexed | 2024-09-23T11:20:08Z |
| format | Thesis |
| id | mit-1721.1/44713 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:20:08Z |
| publishDate | 2009 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/447132019-04-12T09:26:10Z Robust and efficient robotic mapping Olson, Edwin B. (Edwin Brock), 1977- Seth Teller and John Leonard. 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 (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. Includes bibliographical references (p. 123-129). Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while large-scale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have immediate and important applications to autonomous vehicles, precision surveying, and domestic robots. Building accurate maps can be cast as an optimization problem: find the map that is most probable given the set of observations of the environment. However, the problem rapidly becomes difficult when dealing with large maps or large numbers of observations. Sensor noise and non-linearities make the problem even more difficult especially when using inexpensive (and therefore preferable) sensors. This thesis describes an optimization algorithm that can rapidly estimate the maximum likelihood map given a set of observations. The algorithm, which iteratively reduces map error by considering a single observation at a time, scales well to large environments with many observations. The approach is particularly robust to noise and non-linearities, quickly escaping local minima that trap current methods. Both batch and online versions of the algorithm are described. In order to build a map, however, a robot must first be able to recognize places that it has previously seen. Limitations in sensor processing algorithms, coupled with environmental ambiguity, make this difficult. Incorrect place recognitions can rapidly lead to divergence of the map. This thesis describes a place recognition algorithm that can robustly handle ambiguous data. We evaluate these algorithms on a number of challenging datasets and provide quantitative comparisons to other state-of-the-art methods, illustrating the advantages of our methods. by Edwin B. Olson. Ph.D. 2009-03-16T19:33:04Z 2009-03-16T19:33:04Z 2008 2008 Thesis http://hdl.handle.net/1721.1/44713 297537764 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 129 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Olson, Edwin B. (Edwin Brock), 1977- Robust and efficient robotic mapping |
| title | Robust and efficient robotic mapping |
| title_full | Robust and efficient robotic mapping |
| title_fullStr | Robust and efficient robotic mapping |
| title_full_unstemmed | Robust and efficient robotic mapping |
| title_short | Robust and efficient robotic mapping |
| title_sort | robust and efficient robotic mapping |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/44713 |
| work_keys_str_mv | AT olsonedwinbedwinbrock1977 robustandefficientroboticmapping |