We present a semantically rich graph representation for indoor robotic
navigation. Our graph representation encodes: semantic locations such as
offices or corridors as nodes, and navigational behaviors such as enter office
or cross a corridor as edges. In particular, our navigational behaviors operate
directly from visual inputs to produce motor controls and are implemented with
deep learning architectures. This enables the robot to avoid explicit
computation of its precise location or the geometry of the environment, and
enables navigation at a higher level of semantic abstraction. We evaluate the
effectiveness of our representation by simulating navigation tasks in a large
number of virtual environments. Our results show that using a simple sets of
perceptual and navigational behaviors, the proposed approach can successfully
guide the way of the robot as it completes navigational missions such as going
to a specific office. Furthermore, our implementation shows to be effective to
control the selection and switching of behaviors.

本文提出了一种适用于室内机器人导航的语义图形表示方法，该表示方法以语义位置和导航行为为节点和边，利用深度学习架构直接从视觉输入生成导航行为，无需计算机器人精确位置和环境几何关系，实现以更高的语义抽象水平导航的目的。