Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition
This addresses the need for reliable localization in GNSS-denied environments by eliminating manual tuning and adapting to environmental changes, though it is incremental as it builds on existing VPR techniques.
The paper tackles the problem of selecting image matching thresholds in Visual Place Recognition to balance precision and recall, proposing a method that automatically selects operating points based on user-defined precision, resulting in up to 25% higher recall in high-precision regimes.
Visual Place Recognition (VPR) is a key component for localisation in GNSS-denied environments, but its performance critically depends on selecting an image matching threshold (operating point) that balances precision and recall. Thresholds are typically hand-tuned offline for a specific environment and fixed during deployment, leading to degraded performance under environmental change. We propose a method that, given a user-defined precision requirement, automatically selects the operating point of a VPR system to maximise recall. The method uses a small calibration traversal with known correspondences and transfers thresholds to deployment via quantile normalisation of similarity score distributions. This quantile transfer ensures that thresholds remain stable across calibration sizes and query subsets, making the method robust to sampling variability. Experiments with multiple state-of-the-art VPR techniques and datasets show that the proposed approach consistently outperforms the state-of-the-art, delivering up to 25% higher recall in high-precision operating regimes. The method eliminates manual tuning by adapting to new environments and generalising across operating conditions. Our code will be released upon acceptance.