分位点転送による視覚的場所認識の信頼性の高い動作点選択
Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition
視覚的場所認識システムの動作点（マッチング閾値）を、キャリブレーション走行と分位点正規化を用いて自動推定し、環境変化に適応させる手法を提案。
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著者: Dhyey Manish Rajani, Michael Milford, Tobias Fischer
分類: cs.RO, cs.CV
原文アブストラクト
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.
