Functional-SLAM improves real-time robot mapping with interaction knowledge
Functional-SLAM: Interaction-Aware Mapping with Online Functional Scene Graphs
Robotics
Summary
Robots need to understand how things in a room relate to each other to interact well, but current mapping systems don’t capture these functional relationships in real-time. The authors developed Functional-SLAM, a system that builds maps showing how objects and interactions connect while the robot explores. This method helps the robot remember connections better, even in tricky visual environments where things look similar or unclear. Their system runs faster than previous methods and helps the robot know its location more accurately by using these functional links.
SLAMfunctional scene graphrobotic interactionloop closureonline mappingkeyframe geometrypose estimationtopologyvisual reconstruction
Authors
Xinggang Hu, Chenyangguang Zhang, Zihan Zhu, Ruida Zhang, Xiangkui Zhang, Xiangyang Ji
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.