发表机构
Tsinghua University; Dalian University of Technology; ETH Zurich（清华大学; 大连理工大学; 苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Functional-SLAM提出首个在线维护功能场景图的SLAM框架，结合锚点关键帧与功能约束，利用时间关系累积证据并补充回环，实现高效稳定建图与高精度位姿估计。
AI 中文摘要
现有的SLAM系统缺乏对精细机器人交互所需功能关系的建模。功能3D场景图可以表示对象与交互元素之间的关系，但现有方法依赖离线重建，使其无法满足真实世界探索中实时交互的需求。为解决这一局限，我们提出Functional-SLAM，这是首个将功能场景图作为在线SLAM状态持续、递归维护的框架。该框架结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提交稳定的功能边，并在外观重复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能在线高效构建稳定的功能地图，相比离线方法大幅提升运行时间，同时保持极具竞争力的精度。与同类SLAM系统相比，它通过功能拓扑辅助回环进一步提升了位姿估计精度。代码已公开于该https URL。
英文摘要
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.
