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

Junhyun Nam; Wonse Jo

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arXiv · 2610.05757

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

Junhyun Nam·Wonse Jo

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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

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Junhyun Nam, Wonse Jo. 2026-10-05. Human-in-the-Loop Neuro-Symbolic Drift Anticipation for Reliable Visual SLAM. https://arxiv.org/abs/2610.05757

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