实时构建带功能关系的3D场景图，让机器人更好理解环境互动。
Functional-SLAM: Interaction-Aware Mapping with Online Functional Scene Graphs
- 在线递归维护功能场景图，结合几何与功能约束保持节点稳定。
- 利用多帧时序证据确认稳定的功能边，提升地图可靠性。
- 在重复纹理或弱纹理环境下，靠功能拓扑辅助闭环检测，适合真实场景应用。
现有SLAM系统缺乏对精细机器人交互所需功能关系的建模。功能性3D场景图可表征物体与交互元素之间的关系，但现有方法依赖离线重建，难以满足真实世界探索中的实时交互需求。为此，我们提出Functional-SLAM，首个持续递归维护功能场景图作为在线SLAM状态的框架。该框架融合锚点-关键帧几何与功能上下文约束以实现持久节点维护，通过时间关系累积多帧证据以确立稳定的函数边，并在具有重复外观或纹理退化的场景中，用功能拓扑补充视觉回环候选。实验表明，Functional-SLAM能高效在线构建稳定的功能地图，运行效率显著优于离线方法，同时保持高度竞争性的精度。相较于同类SLAM系统，其通过功能拓扑辅助回环闭合进一步提升了位姿估计精度。代码已公开于https://github.com/Hbelief1998/Functional-SLAM-CoRL_2026。
原文摘要 · Abstract (English)
Existing SLAM systems lack modeling of the functional relations required for fine-grained robotic interaction. Functional 3D scene graphs can represent relations between objects and interaction elements, but existing methods rely on offline reconstruction, making them inadequate for real-time interaction in real-world exploration. To address this limitation, we propose Functional-SLAM, the first framework that continuously and recursively maintains a functional scene graph as an online SLAM state. The framework combines anchor-keyframe geometry with functional-context constraints for persistent node maintenance, accumulates multi-frame evidence through temporal relations to commit stable functional edges, and supplements visual loop-closure candidates with functional topology in scenes with repetitive appearance or degraded texture. Experiments show that Functional-SLAM efficiently constructs stable functional maps online, substantially improving runtime over offline methods while maintaining highly competitive accuracy. Compared with peer SLAM systems, it further improves pose estimation accuracy through functional-topology-assisted loop closure. The code is publicly available at https://github.com/Hbelief1998/Functional-SLAM-CoRL_2026.
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