Breaking Déjà Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning
Visual place recognition (VPR) is a key enabler of accurate localization and long-term autonomous navigation in robotics applications, such as loop closure detection for simultaneous localisation and mapping (SLAM). However, real-world VPR deployment relies on selecting an image matching threshold that balances precision and recall. These thresholds are typically tuned using labeled validation data and fixed during deployment, making them unreliable under environmental changes where ground truth is unavailable. This is particularly problematic in safety-critical robotics, where accepting a fal
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- PossiblePossibly related (embedding) · 52%Showcase: geolocating a dashcam video without GPS, only from the footage [P] →
- PossiblePossibly related (embedding) · 45%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
- LinkedLinked via arxiv author · 85%Sania Waheed →
“Breaking Déjà Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning”
- LinkedLinked via arxiv author · 85%Michael Milford →
“Breaking Déjà Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning”
- LinkedLinked via arxiv author · 85%Sarvapali D. Ramchurn →
“Breaking Déjà Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning”
- LinkedLinked via arxiv author · 85%Shoaib Ehsan →
“Breaking Déjà Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning”