Foundation Models for Visual Place Recognition

Abstract

Imagine a robot navigating a complex and unfamiliar environment - underwater, subterranean, or even a dilapidated building. Current Visual Place Recognition (VPR) techniques often struggle with such diverse scenarios, requiring re-training for each new environment. This limitation hinders the development of truly autonomous robots. This work introduces AnyLoc, a novel VPR system taking a significant step towards universality. AnyLoc leverages feature representations learned by powerful foundation models, eliminating the need for VPR-specific training. Furthermore, by combining these features with unsupervised aggregation techniques, AnyLoc can uncover unique visual characteristics that define different environments. Our experiments demonstrate AnyLoc’s potential to function across multiple environments (outdoor, indoor, aerial, underwater, subterranean, and dilapidated) without retraining, laying the groundwork for VPR solutions that could be deployed anywhere, anytime, and across anyview.