  Location estimation is a fundamental sensing task in robotic applications,
where the world is uncertain, and sensors and effectors are noisy. Most systems
make various assumptions about the dependencies between state variables, and
especially about how these dependencies change as a result of actions. Building
on a general framework by Bacchus, Halpern and Levesque for reasoning about
degrees of belief in the situation calculus, and a recent extension to it for
continuous domains, in this paper we illustrate location estimation in the
presence of a rich theory of actions using an example. We also show that while
actions might affect prior distributions in nonstandard ways, suitable
posterior beliefs are nonetheless entailed as a side-effect of the overall
specification.
