BEVGM: A Visual Place Recognition Method With Bird's Eye View Graph Matching

Visual place recognition (VPR) is an essential tool in robotics perception and navigation. Though much progress has been made recently, the performance of VPR is far from satisfactory in challenging scenarios, such as large appearance variations, reverse viewpoints, and heterogeneous data. This work aims to fully leverage semantic and spatial information to achieve more robust and accurate VPR in these challenging scenarios. To this end, we propose a novel bird's eye view (BEV) graph matching based pipeline, which represents a scene as a unified BEV graph that can better integrate appearance, semantics, and spatial structure of the scene. Following a coarse-to-fine hierarchical paradigm, we first search the top
$N$
candidates based on global descriptors. Then, we construct BEV graphs, and formulate the similarity measurement of a query-candidate pair as a quadratic assignment problem, for which an iterative solver taking geometric consistency into account is designed. Further, we propose a Shannon entropy based adaptive fusion strategy to fuse the similarity scores from the coarse and fine matching stages. Extensive evaluation across multiple datasets demonstrates the superiority of our method in various challenging scenarios.