  Using the spatial structure of various indoor environments as prior
knowledge, the robot would construct the map more efficiently. Autonomous
mobile robots generally apply simultaneous localization and mapping (SLAM)
methods to understand the reachable area in newly visited environments.
However, conventional mapping approaches are limited by only considering sensor
observation and control signals to estimate the current environment map. This
paper proposes a novel SLAM method, map completion network-based SLAM
(MCN-SLAM), based on a probabilistic generative model incorporating deep neural
networks for map completion. These map completion networks are primarily
trained in the framework of generative adversarial networks (GANs) to extract
the global structure of large amounts of existing map data. We show in
experiments that the proposed method can estimate the environment map 1.3 times
better than the previous SLAM methods in the situation of partial observation.
