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We consider exploration tasks in which an autonomous mobile robotincrementally builds maps of initially unknown indoor environments. In suchtasks, 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. Inthis paper, we present an approach that exploits a prediction of the geometricstructure of the unknown parts of an environment to improve explorationperformance. In particular, we leverage an existing method that reconstructsthe layout of an environment starting from a partial grid map and that predictsthe shape of partially observed rooms on the basis of geometric featuresrepresenting the regularities of the indoor environment. Then, we originallyemploy the predicted layout to estimate the amount of new area the robot wouldobserve from candidate locations in order to inform the selection of the nextbest location and to early stop the exploration when no further relevant areais expected to be discovered. Experimental activities show that our approach isable to effectively predict the layout of partially observed rooms and to usesuch knowledge to speed up the exploration.

Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms