  We consider exploration tasks in which an autonomous mobile robot
incrementally builds maps of initially unknown indoor environments. In such
tasks, the robot makes a sequence of decisions on where to move next that,
usually, are based on knowledge about the observed parts of the environment. In
this paper, we present an approach that exploits a prediction of the geometric
structure of the unknown parts of an environment to improve exploration
performance. In particular, we leverage an existing method that reconstructs
the layout of an environment starting from a partial grid map and that predicts
the shape of partially observed rooms on the basis of geometric features
representing the regularities of the indoor environment. Then, we originally
employ the predicted layout to estimate the amount of new area the robot would
observe from candidate locations in order to inform the selection of the next
best location and to early stop the exploration when no further relevant area
is expected to be discovered. Experimental activities show that our approach is
able to effectively predict the layout of partially observed rooms and to use
such knowledge to speed up the exploration.
