  Wide-angle sonar mapping of the environment by mobile robot is nontrivial due
to several sources of uncertainty: dropouts due to "specular" reflections,
obstacle location uncertainty due to the wide beam, and distance measurement
error. Earlier papers address the latter problems, but dropouts remain a
problem in many environments. We present an approach that lifts the
overoptimistic independence assumption used in earlier work, and use Bayes nets
to represent the dependencies between objects of the model. Objects of the
model consist of readings, and of regions in which "quasi location invariance"
of the (possible) obstacles exists, with respect to the readings. Simulation
supports the method's feasibility. The model is readily extensible to allow for
prior distributions, as well as other types of sensing operations.
