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

Junhyun Nam; Wonse Jo

cs.ROOct 5, 2026

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

Authors: Junhyun Nam, Wonse Jo

Organizations: Department of Information and Telecommunication Engineering, Incheon National University, Incheon, South Korea 22012.

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

Figures & tables

Figure 1: Trajectory comparison between the ground truth and V-SLAM estimation on the EuRoC MH_04 sequence, illustrating the occurrence of cumulative tracking drift.

Figure 2: Overall architecture of the Hybrid DeepSEE (HDS) framework. The system integrates real-time SLAM features with language-mediated symbolic constraints to proactively anticipate drift risk.

Figure 3: Evaluation of the Early Warning Lead Time and Early Warning Rate (EWR) under varying qualities of language-mediated human annotations.

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Why does Deep Learning Improve Visual SLAM?

Giovanni Cioffi, Davide Scaramuzza

Robotics and Perception Group, Department of Informatics, University of Zurich, Switzerland

Orb-Slam2 Robotic Perception Deep Learning

Open paper AskPDF arXiv

Visual SLAM is a well-established technology utilized in a wide range of real-world applications. However, its performance still degrades under challenging visual conditions, such as low texture, severe motion blur, and poor illumination. Systems based on deep learning outperform classical geometry-based ones and achieve state-of-the-art results by combining learned 2D data association and uncertainty with differentiable geometric optimization in recurrent architectures. Still, it remains unclear exactly which components are fundamentally responsible for this success. In this paper, we ask: Is the superior performance of deep learning-based systems driven primarily by learned 2D data association, the combination of learned 2D data association and uncertainty, or the recurrent architecture itself? We investigate this question empirically by conducting a controlled study. Our findings reveal that the success of DL-based V-SLAM systems hinges on learned 2D data association and uncertainty rather than their recurrent architecture, underscoring the necessity of learning-based paradigms for the design of these components.

Abstract

Explore similar work

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