A deep learning approach to state estimation from videos
| Autor: | Doshi, Chandani |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Druh dokumentu: | Diplomová práce |
| Popis: | 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. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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