A deep learning approach to state estimation from videos
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119761 |
| _version_ | 1826196953893961728 |
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| author | Doshi, Chandani |
| author2 | Rebecca L. Russell and Leslie P. Kaelbling. |
| author_facet | Rebecca L. Russell and Leslie P. Kaelbling. Doshi, Chandani |
| author_sort | Doshi, Chandani |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T10:40:12Z |
| format | Thesis |
| id | mit-1721.1/119761 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:40:12Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1197612019-04-12T22:52:27Z A deep learning approach to state estimation from videos Doshi, Chandani Rebecca L. Russell and Leslie P. Kaelbling. 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 45-47). Kalman lters have been commonly used for estimating the state of a vehicle from a video. Multi-State Constraint Kalman Filter (MSCKF) is an EKF-based state estimator that uses feature measurements for pose estimation of a vehicle. These models require a lot of hands-on engineering time to dene the measurement functions. We propose a data-driven approach by training deep neural networks on high-dimensional navigation image data generated from a simulation. We describe a CNN model that robustly learns reliable features from the input and gives promising results to model temporal data. We show that a deep learning approach can be a replacement for the MSCKF model for estimating the velocity of a moving vehicle. by Chandani Doshi. M. Eng. 2018-12-18T19:48:55Z 2018-12-18T19:48:55Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119761 1078782966 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 47 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Doshi, Chandani A deep learning approach to state estimation from videos |
| title | A deep learning approach to state estimation from videos |
| title_full | A deep learning approach to state estimation from videos |
| title_fullStr | A deep learning approach to state estimation from videos |
| title_full_unstemmed | A deep learning approach to state estimation from videos |
| title_short | A deep learning approach to state estimation from videos |
| title_sort | deep learning approach to state estimation from videos |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119761 |
| work_keys_str_mv | AT doshichandani adeeplearningapproachtostateestimationfromvideos AT doshichandani deeplearningapproachtostateestimationfromvideos |