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6 - Variational Inference

from Part I - Estimation Machinery

Published online by Cambridge University Press:  11 January 2024

Timothy D. Barfoot
Affiliation:
University of Toronto
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Summary

This chapter takes a step back and revisits nonlinear estimation through the lens of variational inference, another concept common in the machine learning world. Estimation is posed as minimizing a data-likelihood objective, the Kullback-Leibler divergence between a Gaussian estimate and the true Bayesian posterior. We follow through the consequences of this starting point and show that we can arrive at many of the algorithms presented earlier through appropriate approximations, but can also open the door to new possibilities. For example, a derivative-free batch estimator that uses sigmapoints is discussed. Variational inference also provides a principled approach to learning parameters in our estimators from training data (i.e., parameters of our motion and observation models).

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Chapter
Information
State Estimation for Robotics
Second Edition
, pp. 182 - 214
Publisher: Cambridge University Press
Print publication year: 2024

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