> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# State Estimation & Mapping

> Recursive state estimation, Bayesian filtering, Kalman filters, occupancy mapping, and SLAM.

State estimation and mapping treat a robot's belief about its pose and its world as a probability distribution that gets updated as new measurements arrive. The framing follows Thrun, Burgard, and Fox's *Probabilistic Robotics*, and it underlies everything from localization in a known map to building a map while moving through it.

This chapter covers the recursive Bayesian machinery behind localization and mapping, then walks through the families of filters used in practice, and ends with simultaneous localization and mapping.

<CardGroup cols={2}>
  <Card title="State estimation" icon="arrows-spin" href="/book/state-estimation/index">
    Recursive Bayesian estimation, Bayes filters, Kalman filters, and HMM-based localization.
  </Card>

  <Card title="Recursive state estimation" icon="rotate" href="/aiml-common/lectures/rse/recursive-state-estimation/index">
    The general recursive Bayes filter and its assumptions.
  </Card>

  <Card title="Discrete Bayesian filter" icon="grid" href="/aiml-common/lectures/rse/discrete-bayesian-filter/index">
    The Bayes filter on a finite state space, with a worked grid-world example.
  </Card>

  <Card title="Kalman filters" icon="wave-square" href="/aiml-common/lectures/rse/kalman-filters/index">
    Optimal linear-Gaussian estimation and its extensions to nonlinear systems.
  </Card>

  <Card title="HMM localization" icon="diagram-project" href="/aiml-common/lectures/rse/hmm-localization/index">
    Hidden Markov model formulation of robot localization.
  </Card>

  <Card title="Occupancy mapping" icon="border-all" href="/aiml-common/lectures/rse/occupancy-mapping/index">
    Building probabilistic grid maps from range-sensor measurements.
  </Card>

  <Card title="SLAM" icon="map-location-dot" href="/aiml-common/lectures/rse/slam/index">
    Simultaneous localization and mapping: estimating pose and map together.
  </Card>
</CardGroup>

***

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