  Object visual navigation aims to steer an agent toward a target object based
on visual observations. It is highly desirable to reasonably perceive the
environment and accurately control the agent. In the navigation task, we
introduce an Agent-Centric Relation Graph (ACRG) for learning the visual
representation based on the relationships in the environment. ACRG is a highly
effective structure that consists of two relationships, i.e., the horizontal
relationship among objects and the distance relationship between the agent and
objects . On the one hand, we design the Object Horizontal Relationship Graph
(OHRG) that stores the relative horizontal location among objects. On the other
hand, we propose the Agent-Target Distance Relationship Graph (ATDRG) that
enables the agent to perceive the distance between the target and objects. For
ATDRG, we utilize image depth to obtain the target distance and imply the
vertical location to capture the distance relationship among objects in the
vertical direction. With the above graphs, the agent can perceive the
environment and output navigation actions. Experimental results in the
artificial environment AI2-THOR demonstrate that ACRG significantly outperforms
other state-of-the-art methods in unseen testing environments.
