  We propose a robotic learning system for autonomous exploration and
navigation in unexplored environments. We are motivated by the idea that even
an unseen environment may be familiar from previous experiences in similar
environments. The core of our method, therefore, is a process for building,
predicting, and using probabilistic layout graphs for assisting goal-based
visual navigation. We describe a navigation system that uses the layout
predictions to satisfy high-level goals (e.g. "go to the kitchen") more rapidly
and accurately than the prior art. Our proposed navigation framework comprises
three stages: (1) Perception and Mapping: building a multi-level 3D scene
graph; (2) Prediction: predicting probabilistic 3D scene graph for the
unexplored environment; (3) Navigation: assisting navigation with the graphs.
We test our framework in Matterport3D and show more success and efficient
navigation in unseen environments.
