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

• URL: http://arxiv.org/abs/2605.18047v3
• Date: Thu, 21 May 2026 07:55:42 GMT
• Title: FUSE: A Framework for Unified State Estimation in Vehicular and Robotic SLAM Systems
• Abstract summary: FUSE is a framework for unified state estimation in vehicular and robotic SLAM systems.<n>A LiDAR--IMU instantiation is developed to examine the framework under mixed-rate sensing and directional degeneracy.
• Score: 28.52219500722156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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