  The problem of autonomous indoor mapping is addressed. The goal is to
minimize the time to achieve a predefined percentage of exposure with some
desired level of certainty. The use of a pre-trained generative deep neural
network, acting as a map predictor, in both the path planning and the map
construction is proposed in order to expedite the mapping process. This method
is examined in combination with several frontier-based path planners for two
distinct floorplan datasets. Simulations are run for several configurations of
the integrated map predictor, the results of which reveal that by utilizing the
prediction a significant reduction in mapping time is possible. When the
prediction is integrated in both path planning and map construction processes
it is shown that the mapping time may in some cases be cut by over 50%.
