让机器人理解人与环境的语义关系，实现协同作业时的智能认知。
Cognitive Synergy Architecture: SEGO for Human-Centric Collaborative Robots
- 构建动态认知场景图，融合几何与语义信息
- 实时生成语义一致的地图，支持人机协同任务
- 适合需要理解上下文的人机协作场景
本文提出SEGO（语义图谱本体），一种面向以人为本的协作机器人认知映射架构，将几何感知、语义推理与解释生成统一整合。SEGO构建动态认知场景图，不仅表征环境的空间布局，还包含检测到物体间的语义关系与本体一致性。该架构无缝融合基于SLAM的定位、基于深度学习的物体检测与追踪，以及本体驱动的推理，实现实时且语义连贯的环境建模。
原文摘要 · Abstract (English)
This paper presents SEGO (Semantic Graph Ontology), a cognitive mapping architecture designed to integrate geometric perception, semantic reasoning, and explanation generation into a unified framework for human-centric collaborative robotics. SEGO constructs dynamic cognitive scene graphs that represent not only the spatial configuration of the environment but also the semantic relations and ontological consistency among detected objects. The architecture seamlessly combines SLAM-based localization, deep-learning-based object detection and tracking, and ontology-driven reasoning to enable real-time, semantically coherent mapping.
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