Learning Gaussisan noise models from high-dimensional sensor data with deep neural networks
Author(s)Liu, Katherine Y
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.
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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.
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).
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics.
Massachusetts Institute of Technology
Aeronautics and Astronautics.