Innovation
Methods, ideas, or system contributions that make the work stand out.
Neuro-Symbolic Framework
Visual SLAM
Drift Anticipation
Human-in-the-Loop
Large Language Model
Human-in-the-Loop Neuro-Symbolic Drift Anticipation for Reliable Visual SLAM
🤖 AI Summary
This study addresses the susceptibility of data-driven visual SLAM models to physically inconsistent drift in out-of-distribution environments due to their black-box nature. To overcome this limitation, we propose the HDS framework, introducing a pioneering human-in-the-loop neuro-symbolic architecture. This framework integrates neural drift estimation with symbolic reasoning, leveraging large language models to translate qualitative human context into interpretable symbolic constraints for proactive drift anticipation. By bridging neural perception and symbolic logic, our approach transcends the inherent limitations of purely data-driven paradigms. The proposed method significantly enhances both the reliability and physical consistency of visual SLAM systems operating within complex environments.
📝 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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