LGPR: Local Feature Learning Brings More Generalizable Visual Place Recognition

Abstract

We propose a Visual Place Recognition (VPR) framework by sharing lightweight keypoint extraction modules for local features. Current research on the joint learning of local keypoint matching and VPR is relatively scarce, and the application deployment of real-time spatial computing on edge devices has a high learning cost. There is also a significant spatial structural difference between existing VPR methods and the scenarios in practical applications. To address these issues, we design a joint learning framework for local keypoint extraction and VPR, which shares local features and fuses irregularly distributed key features in space through self-attention and cross-attention mechanisms. Our framework achieves excellent results on several VPR datasets. In particular, we introduce a new VPR dataset, called TJPark, which has a significant spatial information difference from common street view data. Our method demonstrates that local features with strong generalization capabilities effectively help enhance the generalization of VPR. Our open source code and dataset are available at: https://github.com/ShuaiAlger/LGPR.