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.

翻译：视觉位置识别（VPR）是GNSS拒止环境中定位的关键组件，但其性能关键取决于选择能够平衡精确率与召回率的图像匹配阈值（工作点）。阈值通常针对特定环境离线手动调优，并在部署期间固定，导致在环境变化下性能下降。我们提出一种方法，在给定用户定义的精确率要求下，自动选择VPR系统的工作点以最大化召回率。该方法利用一个具有已知对应关系的小型校准遍历，并通过相似性得分分布的分位数归一化将阈值迁移至部署环境。这种分位数迁移确保了阈值在校准规模和查询子集间保持稳定，使方法对采样变异性具有鲁棒性。使用多种先进VPR技术和数据集的实验表明，所提方法始终优于现有技术，在高精确率工作区间内可实现高达25%的召回率提升。该方法通过适应新环境并泛化至不同工作条件，消除了手动调优的需求。我们的代码将在论文录用后开源。