AnyLoc: Towards Universal Visual Place Recognition

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 finetuning. 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
$4\times$
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
.