构建3D物体关系图，提升机器人导航搜索效率
Graph2Nav: 3D Object-Relation Graph Generation to Robot Navigation
- 基于2D语义图技术扩展至3D，实时生成带语义关系的3D场景图
- 在真实环境中实现高精度3D物体定位与关系标注
- 与大模型规划器结合，显著提升机器人寻物任务效率
我们提出Graph2Nav，一种面向真实世界自主导航的实时3D物体关系图生成框架。该框架完整生成3D场景中的物体及其丰富的语义关系，适用于室内外场景。通过将先进的2D全景场景图方法结合3D语义映射技术拓展至3D空间，学习生成物体间的3D语义关系，避免了直接从3D数据中训练3D场景图所面临的训练数据限制。我们通过实验验证了3D物体定位与关系标注的准确性，并将Graph2Nav集成到基于大语言模型的SayNav导航规划器中，在无人地面机器人的真实环境目标搜寻任务中进行了评估。结果表明，在场景图中建模物体关系能显著提升导航任务的搜索效率。
原文摘要 · Abstract (English)
We propose Graph2Nav, a real-time 3D object-relation graph generation framework, for autonomous navigation in the real world. Our framework fully generates and exploits both 3D objects and a rich set of semantic relationships among objects in a 3D layered scene graph, which is applicable to both indoor and outdoor scenes. It learns to generate 3D semantic relations among objects, by leveraging and advancing state-of-the-art 2D panoptic scene graph works into the 3D world via 3D semantic mapping techniques. This approach avoids previous training data constraints in learning 3D scene graphs directly from 3D data. We conduct experiments to validate the accuracy in locating 3D objects and labeling object-relations in our 3D scene graphs. We also evaluate the impact of Graph2Nav via integration with SayNav, a state-of-the-art planner based on large language models, on an unmanned ground robot to object search tasks in real environments. Our results demonstrate that modeling object relations in our scene graphs improves search efficiency in these navigation tasks.
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