Visual Place Recognition (VPR) is vital for robot localization. To date, the most performant VPR approaches are environment- and task-specific: while they exhibit strong performance in structured environments (predominantly urban driving), their performance degrades severely in unstructured environments, rendering most approaches brittle to robust real-world deployment. In this work, we develop a universal solution to VPR -- a technique that works across a broad range of structured and unstructured environments (urban, outdoors, indoors, aerial, underwater, and subterranean environments) without any re-training or fine-tuning. We demonstrate that general-purpose feature representations derived from off-the-shelf self-supervised models with no VPR-specific training are the right substrate upon which to build such a universal VPR solution. Combining these derived features with unsupervised feature aggregation enables our suite of methods, AnyLoc, to achieve up to 4X significantly higher performance than existing approaches. We further obtain a 6% improvement in performance by characterizing the semantic properties of these features, uncovering unique domains which encapsulate datasets from similar environments. Our detailed experiments and analysis lay a foundation for building VPR solutions that may be deployed anywhere, anytime, and across anyview. We encourage the readers to explore our project page and interactive demos: https://anyloc.github.io/.

翻译：视觉地点识别（VPR）对机器人定位至关重要。迄今为止，性能最优的VPR方法通常具有环境和任务特异性：虽然在结构化环境（主要为城市驾驶环境）中表现出色，但在非结构化环境中性能显著下降，导致大多数方法难以适应稳健的实地部署。本研究提出了一种通用VPR解决方案——该技术无需任何重新训练或微调，即可在广泛的结构化和非结构化环境（城市、户外、室内、空中、水下及地下环境）中有效运行。我们证明：从现成的自监督模型中提取的通用特征表示（无需专门针对VPR训练）是构建此类通用VPR方案的正确基础。将这些特征与无监督特征聚合相结合，使我们提出的方法套件AnyLoc能够实现比现有方法高达4倍的性能提升。进一步地，通过表征这些特征的语义属性，我们发现了封装相似环境数据集的独特领域，从而将性能再提升6%。详细的实验与分析为构建可部署于任意地点、任意时间及任意视角的VPR解决方案奠定了基础。欢迎读者访问我们的项目页面和交互式演示：https://anyloc.github.io/。