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：视觉位置识别（VPR）对机器人定位至关重要。目前，性能最优的VPR方法通常针对特定环境和任务设计：虽然在结构化环境（主要是城市驾驶）中表现强劲，但在非结构化环境中性能严重下降，导致多数方法难以适应稳健的现实部署。本文提出了一种通用的VPR解决方案——该技术无需重新训练或微调，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）中工作。我们证明，源自现成自监督模型（无需VPR特定训练）的通用特征表示是构建此类通用VPR方案的理想基础。将这些派生特征与无监督特征聚合相结合，使我们的方法套件AnyLoc相比现有方法取得了高达4倍显著性能提升。通过表征这些特征的语义属性，我们进一步获得了6%的性能改进，并发现了封装相似环境数据集的独特领域。我们的详细实验与分析为构建可部署于任何地点、任何时间、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与交互式演示：https://anyloc.github.io/。