Passive object recognition using intrinsic shape signatures
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2011
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| Online Access: | http://hdl.handle.net/1721.1/66417 |
| _version_ | 1826204597889269760 |
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| author | Donahue, Kenneth M. (Kenneth Michael) |
| author2 | Seth Teller. |
| author_facet | Seth Teller. Donahue, Kenneth M. (Kenneth Michael) |
| author_sort | Donahue, Kenneth M. (Kenneth Michael) |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. |
| first_indexed | 2024-09-23T12:57:40Z |
| format | Thesis |
| id | mit-1721.1/66417 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:57:40Z |
| publishDate | 2011 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/664172019-04-11T03:23:50Z Passive object recognition using intrinsic shape signatures Donahue, Kenneth M. (Kenneth Michael) Seth Teller. 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 (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 53). The Agile Robotics Group (AR) at MIT's Computer Science and Artificial Intelligence Laboratories (CSAIL) has an autonomous, forklift capable of doing task planning, navigation, obstacle detection and avoidance, focused object detection, etc. The goal of the project is to have a completely autonomous robot that is safe to use in a human environment. One aspect of the project which would be very beneficial to moving on to more complicated tasks is passive object recognition. The forklift is capable of doing a focused scan and looking for very particular things. The forklift is constantly scanning its vicinity with its Light Detection and Ranging (LiDAR) sensors to ensure that it avoids obstacles; instead of only using that information for hazard avoidance, that information can be used to not only passively notice objects but also classify them. This will be useful later when the team starts implementing various higher-level processes, such as localization and/or mapping. This paper will discuss various modules that were integrated into the Agile Robotics infrastructure that made object recognition possible. These modules were 1) a data segmentation module, 2) an object recognition module using Intrinsic Shape Signature[10] (ISS) to find feature points in our LiDAR data, and 3) various visualization modules to ensure that each module was behaving properly. by Kenneth M. Donahue. M.Eng. 2011-10-17T21:23:42Z 2011-10-17T21:23:42Z 2011 2011 Thesis http://hdl.handle.net/1721.1/66417 755092231 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 53 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Donahue, Kenneth M. (Kenneth Michael) Passive object recognition using intrinsic shape signatures |
| title | Passive object recognition using intrinsic shape signatures |
| title_full | Passive object recognition using intrinsic shape signatures |
| title_fullStr | Passive object recognition using intrinsic shape signatures |
| title_full_unstemmed | Passive object recognition using intrinsic shape signatures |
| title_short | Passive object recognition using intrinsic shape signatures |
| title_sort | passive object recognition using intrinsic shape signatures |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/66417 |
| work_keys_str_mv | AT donahuekennethmkennethmichael passiveobjectrecognitionusingintrinsicshapesignatures |