Simultaneous Tracking, Object Registration, and Mapping (STORM)
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thèse |
| Langue: | eng |
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Massachusetts Institute of Technology
2018
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| Accès en ligne: | http://hdl.handle.net/1721.1/119560 |
| _version_ | 1826215480776458240 |
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| author | Kee, Vincent P |
| author2 | Gian Luca Mariottini and Sertac Karaman. |
| author_facet | Gian Luca Mariottini and Sertac Karaman. Kee, Vincent P |
| author_sort | Kee, Vincent P |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T16:31:06Z |
| format | Thesis |
| id | mit-1721.1/119560 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:31:06Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1195602019-04-12T07:45:23Z Simultaneous Tracking, Object Registration, and Mapping (STORM) STORM Kee, Vincent P Gian Luca Mariottini and Sertac Karaman. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 87-91). An autonomous system needs to be aware of its surroundings and know where it is in its environment in order to operate robustly in unknown environments. This problem is known as Simultaneous Localization and Mapping (SLAM). SLAM techniques have been successfully implemented on systems operating in the real world. However, most SLAM approaches assume that the environment does not change during operation -- the static world assumption. When this assumption is violated (e.g. an object moves), the SLAM estimate degrades. Consequently, the static world assumption prevents robots from interacting with their environments (e.g. manipulating objects) and restricts them to navigating in static environments. Additionally, most SLAM systems generate maps composed of low-level features that lack information about objects and their locations in the scene. This representation limits the map's utility, preventing it from being used for tasks beyond navigation such as object manipulation and task planning. We present Simultaneous Tracking, Object Registration, and Mapping (STORM), a SLAM system that represents an environment as a collection of dynamic objects. STORM enables a robot to build and maintain maps of dynamic environments, use the map estimates to manipulate objects, and localize itself in the map when revisiting the environment. We demonstrate STORM's capabilities with simulation and real-world experiments and compare its performance against that of a typical SLAM approach. by Vincent P. Kee. M. Eng. 2018-12-11T20:40:09Z 2018-12-11T20:40:09Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119560 1076274356 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 91 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Kee, Vincent P Simultaneous Tracking, Object Registration, and Mapping (STORM) |
| title | Simultaneous Tracking, Object Registration, and Mapping (STORM) |
| title_full | Simultaneous Tracking, Object Registration, and Mapping (STORM) |
| title_fullStr | Simultaneous Tracking, Object Registration, and Mapping (STORM) |
| title_full_unstemmed | Simultaneous Tracking, Object Registration, and Mapping (STORM) |
| title_short | Simultaneous Tracking, Object Registration, and Mapping (STORM) |
| title_sort | simultaneous tracking object registration and mapping storm |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119560 |
| work_keys_str_mv | AT keevincentp simultaneoustrackingobjectregistrationandmappingstorm AT keevincentp storm |