Visual Place Recognition (VPR) enables robots and autonomous vehicles to identify previously visited locations by matching current observations against a database of known places. However, VPR systems face significant challenges when deployed across varying visual environments, lighting conditions, seasonal changes, and viewpoints changes. Failure-critical VPR applications, such as loop closure detection in simultaneous localization and mapping (SLAM) pipelines, require robust estimation of place matching uncertainty. We propose three training-free uncertainty metrics that estimate prediction confidence by analyzing inherent statistical patterns in similarity scores from any existing VPR method. Similarity Distribution (SD) quantifies match distinctiveness by measuring score separation between candidates; Ratio Spread (RS) evaluates competitive ambiguity among top-scoring locations; and Statistical Uncertainty (SU) is a combination of SD and RS that provides a unified metric that generalizes across datasets and VPR methods without requiring validation data to select the optimal metric. All three metrics operate without additional model training, architectural modifications, or computationally expensive geometric verification. Comprehensive evaluation across nine state-of-the-art VPR methods and six benchmark datasets confirms that our metrics excel at discriminating between correct and incorrect VPR matches, and consistently outperform existing approaches while maintaining negligible computational overhead, making it deployable for real-time robotic applications across varied environmental conditions with improved precision-recall performance.

翻译：视觉地点识别（VPR）使机器人和自动驾驶车辆能够通过将当前观测与已知地点数据库进行匹配来识别先前访问过的位置。然而，VPR系统在部署于不同视觉环境、光照条件、季节变化和视角变化时面临重大挑战。对于故障关键型VPR应用（例如同时定位与建图（SLAM）流程中的回环检测），需要对地点匹配不确定性进行鲁棒估计。我们提出了三种免训练的不确定性度量方法，通过分析任何现有VPR方法相似性分数的固有统计模式来估计预测置信度：相似性分布（SD）通过测量候选得分之间的分离度来量化匹配区分度；比率展宽（RS）评估高分位置间的竞争模糊性；统计不确定性（SU）是SD与RS的组合，提供了一个统一度量，无需验证数据选择最优指标即可跨数据集和VPR方法泛化。所有三种度量均无需额外模型训练、架构修改或计算昂贵的几何验证。通过对九种前沿VPR方法和六个基准数据集的综合评估证实，我们的度量在区分正确与错误VPR匹配方面表现卓越，始终优于现有方法，同时保持可忽略的计算开销，使其能够部署于各种环境条件下的实时机器人应用，并提升查准率-查全率性能。