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The challenge of mapping indoor environments is addressed. Typical heuristicalgorithms for solving the motion planning problem are frontier-based methods,that are especially effective when the environment is completely unknown.However, in cases where prior statistical data on the environment'sarchitectonic features is available, such algorithms can be far from optimal.Furthermore, their calculation time may increase substantially as more areasare exposed. In this paper we propose two means by which to overcome theseshortcomings. One is the use of deep reinforcement learning to train the motionplanner. The second is the inclusion of a pre-trained generative deep neuralnetwork, acting as a map predictor. Each one helps to improve the decisionmaking through use of the learned structural statistics of the environment, andboth, being realized as neural networks, ensure a constant calculation time. Weshow that combining the two methods can shorten the duration of the mappingprocess by up to 4 times, compared to frontier-based motion planning.

Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping