2026-09-18T20:55:27Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1156772022-01-13T07:53:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nicholas Roy.
Liu, Katherine Y
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2018-05-23T16:29:51Z
2018-05-23T16:29:51Z
2018
2018
http://hdl.handle.net/1721.1/115677
1036985591
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.
92 pages
eng
Massachusetts Institute of Technology
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
Aeronautics and Astronautics.
Learning Gaussisan noise models from high-dimensional sensor data with deep neural networks
Thesis
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