Functional-SLAM: Interaction-Aware Mapping with Online Functional Scene Graphs

2026-09-07

AI Summary

为解决现有SLAM系统缺乏对细粒度机器人交互所需的功能关系建模的问题，提出Functional-SLAM框架，通过在线维护功能场景图来实现实时互动。

Abstract

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.

Problem

• SLAM
• functional relations
• real-time interaction
• offline reconstruction
• 3D scene graphs

Innovation

• Functional-SLAM
• online functional scene graph
• temporal relations
• functional topology
• loop closure