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

2026-05-18

AI Summary

This work addresses the challenge in conventional tightly coupled SLAM systems, where time handling, geometric association, estimator design, and map updating are highly interdependent, hindering independent optimization. To overcome this, the paper introduces FUSE, a novel framework that decouples core SLAM state estimation components into standardized interfaces for the first time, defining unified mechanisms for observation input, state propagation, update, and query. This modular design enables flexible component substitution. A LiDAR–IMU system built upon FUSE integrates high-frequency IMU propagation, LiDAR-triggered geometric updates, residual filtering, and degeneracy-aware correction. Evaluated on a 418-meter looped corridor sequence, it achieves an end-to-end trajectory error of 1.626 meters, representing a 7.9% reduction in relative error compared to the best-performing baseline, Faster-LIO.

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 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.

Problem

• SLAM
• state estimation
• mixed-rate sensing
• design modularity
• temporal processing

Innovation

• unified state estimation
• modular SLAM framework
• mixed-rate sensing
• degeneracy-aware correction
• residual-ready geometric association