Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition

2026-07-23 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new quantile transfer method enhances Visual Place Recognition (VPR) systems by automatically selecting optimal image matching thresholds to maximize recall under user-defined precision requirements. This approach addresses the common issue of performance degradation in VPR due to fixed thresholds under changing environmental conditions. The method utilizes a small calibration traversal with known correspondences, transferring thresholds to deployment via quantile normalization of similarity score distributions, ensuring stability across calibration sizes and query subsets. Evaluated against seven state-of-the-art VPR techniques, including MegaLoc and NetVLAD, and three benchmark datasets like Nordland and Oxford RobotCar, the proposed system consistently outperforms existing methods. It achieves up to 25% higher recall in high-precision operating regimes, effectively eliminating manual tuning and adapting to diverse environments. The system processes queries rapidly, with runtimes as low as 0.001s per query on an Intel i7-14700 processor.

Key takeaway

For Robotics Engineers deploying Visual Place Recognition in GNSS-denied or dynamic environments, you should adopt adaptive thresholding techniques. This quantile transfer method allows your VPR system to automatically maximize recall while strictly adhering to your specified precision requirements, crucial for safety-critical applications. By leveraging a small calibration set, you can eliminate manual tuning and ensure robust performance across varying conditions, significantly reducing false positives and improving localization reliability.

Key insights

Quantile transfer robustly adapts VPR matching thresholds to maximize recall under user-defined precision, outperforming static methods.

Principles

• Relative similarity ordering is more stable than absolute scores.
• Quantile normalization enables robust threshold transfer across environments.
• High-precision operating points require stable threshold selection.

Method

The method involves constructing similarity matrices, adaptively sampling calibration queries, converting precision-maximizing thresholds into quantiles, and transferring these quantiles to deployment score distributions to select the final operating point.

In practice

• Use a small calibration traversal for threshold estimation.
• Apply quantile normalization for robust threshold adaptation.
• Prioritize high-precision regimes for safety-critical VPR.

Topics

• Visual Place Recognition
• Quantile Transfer
• Operating Point Selection
• Precision-Recall Trade-off
• Mobile Robotics
• GNSS-Denied Environments

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.