11.6 Computing Probabilistic Information States

The probabilistic I-states can be quite complicated in practice
because each element of
is a probability distribution or
density function. Therefore, substantial effort has been invested in
developing efficient techniques for computing probabilistic I-states
efficiently. This section can be considered as a continuation of the
presentations in Sections 11.2.3 (and part of Section
11.4, for the case of continuous state spaces). Section
11.6.1 covers Kalman filtering, which provides elegant
computations of probabilistic I-states. It is designed for
problems in which the state transitions and sensor mapping are linear,
and all acts of nature are modeled by multivariate Gaussian densities.
Section 11.6.2 covers a general sampling-based planning
approach, which is approximate but applies to a broader class of
problems. One of these methods, called *particle filtering*, has
become very popular in recent years for mobile robot localization.

Steven M LaValle 2020-08-14