Visual Place Recognition (VPR) plays a critical role in many localization and mapping pipelines. It consists of retrieving the closest sample to a query image, in a certain embedding space, from a database of geotagged references. The image embedding is learned to effectively describe a place despite variations in visual appearance, viewpoint, and geometric changes. In this work, we formulate how limitations in the Geographic Distance Sensitivity of current VPR embeddings result in a high probability of incorrectly sorting the top-k retrievals, negatively impacting the recall. In order to address this issue in single-stage VPR, we propose a novel mining strategy, CliqueMining, that selects positive and negative examples by sampling cliques from a graph of visually similar images. Our approach boosts the sensitivity of VPR embeddings at small distance ranges, significantly improving the state of the art on relevant benchmarks. In particular, we raise recall@1 from 75% to 82% in MSLS Challenge, and from 76% to 90% in Nordland. Models and code are available at https://github.com/serizba/cliquemining.

翻译：视觉地点识别（VPR）在许多定位与建图流程中扮演着关键角色。其任务在于从地理标记的参考数据库中，在特定嵌入空间内检索出与查询图像最接近的样本。通过学习得到的图像嵌入，能够有效描述一个地点，即使其视觉外观、视点和几何结构发生变化。在本工作中，我们阐述了当前VPR嵌入在地理距离敏感性方面的局限如何导致前k个检索结果排序错误的高概率，从而对召回率产生负面影响。为了解决单阶段VPR中的这一问题，我们提出了一种新颖的挖掘策略——CliqueMining，该策略通过从视觉相似图像构成的图中采样团来选择正负样本。我们的方法提升了VPR嵌入在小距离范围内的敏感性，在相关基准测试上显著改进了现有技术水平。具体而言，我们在MSLS Challenge上将recall@1从75%提升至82%，在Nordland上从76%提升至90%。模型与代码可在 https://github.com/serizba/cliquemining 获取。