将机器人环境数据统一为标准格式，生成可行动知识库。
Generating Actionable Robot Knowledge Bases by Combining 3D Scene Graphs with Robot Ontologies
- 用USD格式统一多种3D场景数据格式
- 实现环境数据与机器人本体论结合，支持实时决策
- 提供可视化工具辅助语义映射，适合机器人研发者
在机器人领域，环境数据的有效整合为可行动知识仍是重大挑战，主要源于场景描述中常用格式（如MJCF、URDF、SDF）的多样性和不兼容性。本文提出一种新方法，通过构建统一场景图模型，将这些异构格式标准化为通用场景描述（USD）格式。该标准化使场景图可与机器人本体论通过语义报告集成，从而将复杂环境数据转化为认知机器人控制所需的动作知识。我们通过将程序化3D环境转换为USD格式，并进行语义标注和知识图谱转换，有效回答能力问题，验证了其在实时机器人决策中的实用性。此外，开发了一个基于Web的可视化工具，支持语义映射过程，为用户提供直观的3D环境管理界面。
原文摘要 · Abstract (English)
In robotics, the effective integration of environmental data into actionable knowledge remains a significant challenge due to the variety and incompatibility of data formats commonly used in scene descriptions, such as MJCF, URDF, and SDF. This paper presents a novel approach that addresses these challenges by developing a unified scene graph model that standardizes these varied formats into the Universal Scene Description (USD) format. This standardization facilitates the integration of these scene graphs with robot ontologies through semantic reporting, enabling the translation of complex environmental data into actionable knowledge essential for cognitive robotic control. We evaluated our approach by converting procedural 3D environments into USD format, which is then annotated semantically and translated into a knowledge graph to effectively answer competency questions, demonstrating its utility for real-time robotic decision-making. Additionally, we developed a web-based visualization tool to support the semantic mapping process, providing users with an intuitive interface to manage the 3D environment.
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