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This chapter covers state estimation techniques essential for robotics, including Bayesian filtering and Kalman filters.

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Topics

Hidden Markov Models

Introduction to HMMs for state estimation.

Bayes Filter

The fundamental Bayesian filtering algorithm.

Kalman Filter

Optimal state estimation under Gaussian assumptions.

Particle Filters

Monte Carlo methods for non-linear state estimation.
Video lectures on state estimation are available in the Media section under the Robotics course.
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