Object goal navigation (ObjectNav) is the task of finding a target object in an unseen environment. It is one of the fundamental challenges in visual navigation as it requires both structural and semantic understanding. In this letter, we present OVG-Nav, a novel ObjectNav framework that leverages a topological graph structure called object value graph (OVG), which contains visual observations and commonsense prior knowledge. The high-level planning of OVG-Nav prioritizes subgoal nodes for exploration based on a metric called object value , which reflects the closeness to the target object. Here, we propose OVGNet, a model designed to predict the object values of each node of an OVG using observed features along with commonsense knowledge. The structure of high-level planning using OVG and low-level action decisions reduces sensitivity to accumulating sensor noises, leading to robust navigation performance. Experimental results show that OVG-Nav outperforms the baseline in success rate (SR) and success rate weighted by path length (SPL) in the MP3D dataset both in accurate sensing and noisy sensing. In addition, we show that the OVG-Nav can be transferred to the real-world robot successfully.

中文翻译：

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用于对象目标导航的常识感知对象值图

对象目标导航（ObjectNav）是在看不见的环境中寻找目标对象的任务。这是视觉导航的基本挑战之一，因为它需要结构和语义理解。在这封信中，我们介绍了 OVG-Nav，这是一种新颖的 ObjectNav 框架，它利用称为对象值图 (OVG) 的拓扑图结构，其中包含视觉观察和常识性先验知识。 OVG-Nav 的高层规划根据称为 的指标对探索的子目标节点进行优先级排序对象值，反映与目标对象的接近程度。在这里，我们提出了 OVGNet，这是一种模型，旨在使用观察到的特征和常识知识来预测 OVG 每个节点的对象值。使用 OVG 的高层规划结构和低层行动决策降低了对累积传感器噪声的敏感度，从而实现稳健的导航性能。实验结果表明，OVG-Nav 在准确传感和噪声传感方面均优于 MP3D 数据集中的成功率（SR）和按路径长度加权的成功率（SPL）基线。此外，我们还表明 OVG-Nav 可以成功转移到现实世界的机器人上。