Predicting the Layout of Partially Observed Rooms from Grid Maps

In several applications, autonomous mobile robots benefit from knowing the structure of the indoor environments where they operate. This knowledge can be extracted from the metric maps built (e.g., using SLAM algorithms) from the data perceived by the robots' sensors. The layout is a way to represent the structure of an indoor environment with geometrical primitives. Most of the current methods for reconstructing the layout from a metric map represent the parts of the environment that have been fully observed. In this paper, we propose an approach that predicts the layout of rooms which are only partially known in a 2D metric grid map. The prediction is made according to the global structure of the environment, as identified from its known parts. Experiments show that our approach is able to effectively predict the layout of several indoor environments that have been observed to different degrees.