视觉地点识别中可靠操作点选择的分位数迁移
Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition
AI总结：
提出一种通过分位数归一化迁移阈值的方法，自动选择视觉地点识别系统的操作点，在100%精度下最大化召回率，无需手动调参。
摘要：
视觉地点识别（VPR）是全球导航卫星系统（GNSS）受限环境中定位的关键组成部分，但其性能严重依赖于选择平衡精度和召回率的图像匹配阈值（操作点）。阈值通常针对特定环境离线手动调整，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，自动选择VPR系统的操作点，以在100%精度下最大化召回率。该方法使用已知对应关系的小型校准遍历，并通过相似度得分分布的分位数归一化将阈值迁移到部署中。这种分位数迁移确保阈值在校准大小和查询子集上保持稳定。在五个基准数据集上使用七种最先进的VPR技术进行的实验表明，我们提出的方法始终优于现有基线，使底层VPR技术在大约两倍的部署场景中（中位数改进）以100%精度运行，同时在该精度下检索到多达29%的正确匹配。该方法通过适应新环境并在操作条件下泛化，消除了手动调整。我们的代码可在该https URL获取。
英文摘要：
Visual Place Recognition (VPR) is a key component for localization in Global Navigation Satellite System (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 automatically estimates the operating point of a VPR system to maximize recall whilst aiming to achieve 100% precision. The method uses a small calibration traversal with known correspondences and transfers thresholds to deployment via quantile normalization of similarity score distributions. This quantile transfer ensures that thresholds remain stable across calibration sizes and query subsets. Experiments with seven state-of-the-art VPR techniques across five benchmark datasets demonstrate that our proposed approach consistently outperforms existing baselines, enabling the underlying VPR technique to operate at 100% precision in approximately twice as many deployment scenarios (median improvement), while retrieving up to 29% more correct matches at that precision. The method eliminates manual tuning by adapting to new environments and generalizing across operating conditions. Our code is available at https://github.com/DhyeyR-007/Quantile-Transfer-for-Reliable-VPR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
