FUSE: A Framework for Unified State Estimation in Robotic SLAM Systems
For SLAM researchers, FUSE offers a modular framework to independently vary design choices, reducing re-engineering effort.
FUSE provides a unified framework for state estimation in robotic SLAM that decouples temporal processing, geometric association, estimator formulation, and map-update policy. A LiDAR-IMU instantiation achieves 1.626 m end-to-end error on a 418 m loop, a 7.9% relative reduction over Faster-LIO.
Tightly coupled SLAM formulations under mixed-rate sensing often bind temporal processing, local geometric association, estimator formulation, and map-update policy into method-specific designs. Such binding makes it difficult to vary one design choice without re-engineering the rest of the state-estimation process. This paper presents FUSE, a framework for unified state estimation in robotic SLAM systems. FUSE organizes the state-estimation interface around observation ingestion, propagation, update, and state query, and uses this interface to separate temporal processing, residual-ready local geometric association, estimator formulation, and map-update policy. A LiDAR--IMU instantiation is developed to examine the framework under mixed-rate sensing and directional degeneracy, where high-rate inertial propagation, LiDAR-triggered geometric update, residual screening, and degeneracy-aware correction operate through the same interface boundaries. On a 418 m loop-corridor sequence, the instantiation reports a 1.626~m end-to-end trajectory error, corresponding to a 7.9% relative error reduction compared with Faster-LIO, the lowest-error baseline on this sequence. The results support FUSE as a framework for organizing state-estimation design choices and show how the evaluated instantiation regularizes updates along weakly observable directions.