  Place recognition is a challenging but crucial task in robotics. Current
description-based methods may be limited by representation capabilities, while
pairwise similarity-based methods require exhaustive searches, which is
time-consuming. In this paper, we present a novel coarse-to-fine approach to
address these problems, which combines BEV (Bird's Eye View) feature
extraction, coarse-grained matching and fine-grained verification. In the
coarse stage, our approach utilizes an attention-guided network to generate
attention-guided descriptors. We then employ a fast affinity-based candidate
selection process to identify the Top-K most similar candidates. In the fine
stage, we estimate pairwise overlap among the narrowed-down place candidates to
determine the final match. Experimental results on the KITTI and KITTI-360
datasets demonstrate that our approach outperforms state-of-the-art methods.
The code will be released publicly soon.
