  Hidden Markov models (HMMs) and partially observable Markov decision
processes (POMDPs) provide useful tools for modeling dynamical systems. They
are particularly useful for representing the topology of environments such as
road networks and office buildings, which are typical for robot navigation and
planning. The work presented here describes a formal framework for
incorporating readily available odometric information and geometrical
constraints into both the models and the algorithm that learns them. By taking
advantage of such information, learning HMMs/POMDPs can be made to generate
better solutions and require fewer iterations, while being robust in the face
of data reduction. Experimental results, obtained from both simulated and real
robot data, demonstrate the effectiveness of the approach.
