Human-in-the-Loop Neuro-Symbolic Drift Anticipation for Reliable Visual SLAM

Abstract:This paper introduces Hybrid DeepSEE (HDS), a Human-in-the-Loop (HITL) neuro-symbolic framework for proactive drift anticipation in Visual SLAM (V-SLAM). While data-driven models offer predictive power, their "black-box" nature often yields physically inconsistent outputs in out-of-distribution (OOD) environments. To address this, HDS integrates neural drift risk estimation with symbolic constraint reasoning. By utilizing a Large Language Model (LLM) as a reasoning bridge, the framework translates qualitative human context into interpretable symbolic constraints. Building upon this architecture, we propose a superior drift anticipation framework that ensures enhanced reliability and consistency in Visual SLAM

Robotics (cs.RO)

arXiv:2610.05757 [cs.RO]

(or arXiv:2610.05757v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.05757 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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