AnyLoc：迈向通用视觉地点识别
AnyLoc: Towards Universal Visual Place Recognition
AI总结：
AnyLoc利用自监督通用特征与无监督聚合，无需重训练即可在多种结构化和非结构化环境中实现通用视觉地点识别，性能最高提升4倍。
摘要：
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方法往往针对特定环境和任务：虽然它们在结构化环境（主要是城市驾驶）中表现强劲，但在非结构化环境中性能会严重下降，导致大多数方法在真实世界部署中缺乏鲁棒性。在本工作中，我们提出了一种通用的VPR解决方案——一种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、水下和地下环境）中工作的技术。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VPR特定训练）是构建这种通用VPR解决方案的正确基础。将这些提取的特征与无监督特征聚合相结合，使我们的方法套件AnyLoc能够取得比现有方法最高达4倍的显著性能提升。通过刻画这些特征的语义属性，我们进一步获得了6%的性能提升，并发现了能够将来自相似环境的数据集封装起来的独特域。我们详细的实验和分析为构建可随时随地、跨任意视角部署的VPR解决方案奠定了基础。我们鼓励读者探索我们的项目页面和交互式演示：https://anyloc.github.io/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
