Agent-Centric Relation Graph for Object Visual Navigation
Xiaobo Hu, Youfang Lin, Shuo Wang, Zhihao Wu, Kai Lv

TL;DR
This paper introduces an agent-centric relation graph (ACRG) that enhances object visual navigation by modeling horizontal and distance relationships, leading to improved performance in unseen environments.
Contribution
The paper proposes a novel ACRG structure combining horizontal and distance relationship graphs to improve visual navigation accuracy.
Findings
ACRG outperforms state-of-the-art methods in AI2-THOR environments.
The use of depth and vertical cues improves environment perception.
Significant generalization to unseen environments was achieved.
Abstract
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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Domain Adaptation and Few-Shot Learning · Advanced Image and Video Retrieval Techniques
