Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 87-92). / While measurement covariances are often taken to be constant in many robotic state estimation systems, many sensors exhibit different interactions with their environment. Accurate covariance estimation allows graph-based estimation techniques to better optimize state estimates by reasoning about the utility of different methods relative to each other. This thesis describes a method of learning compact feature representations for real-time covariance estimation. A direct log-likelihood optimization technique is used to train a deep convolutional neural network to predict the covariance matrix of a Gaussian measurement model, given representative data. This method is algorithm-agnostic, and therefore does not require the handcoding of representative features. Quantative results are presented, showing that improved measurement covariances on a frame-to-frame visual odometry system reduce trajectory errors after a loop closure is applied. / by Katherine Y. Liu. / S.M.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/115677 |
| Date | January 2018 |
| Creators | Liu, Katherine Y |
| Contributors | Nicholas Roy., Massachusetts Institute of Technology. Department of Aeronautics and Astronautics., Massachusetts Institute of Technology. Department of Aeronautics and Astronautics. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 92 pages, application/pdf |
| Rights | 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 |
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