  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/.
