FUSE: A Framework for Unified State Estimation in Vehicular and Robotic SLAM Systems

TL;DR

FUSE is a flexible framework that unifies state estimation processes in vehicular and robotic SLAM, enabling easier design variation and improved robustness in mixed-rate sensing scenarios.

Contribution

The paper introduces FUSE, a modular framework that separates key SLAM components, facilitating adaptable and robust state estimation in complex sensing environments.

Findings

Achieved 7.9% error reduction over Faster-LIO on a 418m sequence.

Demonstrated effective handling of mixed-rate sensing and directional degeneracy.

Validated the framework's ability to organize and improve SLAM system components.

Abstract

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 vehicular and 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.