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- W2805874067 abstract "While measurement covariances are often taken to beconstant in many robotic state estimation systems, many sensorsexhibit different interactions with their environment. Accuratecovariance estimation allows graph-based estimation techniques tobetter optimize state estimates by reasoning about the utility ofdifferent methods relative to each other. This thesis describes amethod of learning compact feature representations for real-timecovariance estimation. A direct log-likelihood optimizationtechnique is used to train a deep convolutional neural network topredict the covariance matrix of a Gaussian measurement model,given representative data. This method is algorithm-agnostic, andtherefore does not require the handcoding of representativefeatures. Quantative results are presented, showing that improvedmeasurement covariances on a frame-to-frame visual odometry systemreduce trajectory errors after a loop closure isapplied." @default.
- W2805874067 created "2018-06-13" @default.
- W2805874067 creator A5041192339 @default.
- W2805874067 date "2018-01-01" @default.
- W2805874067 modified "2023-09-23" @default.
- W2805874067 title "Learning Gaussisan noise models from high-dimensional sensor data with deep neural networks" @default.
- W2805874067 hasPublicationYear "2018" @default.
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