A Coarse-to-Fine Place Recognition Approach using Attention-guided Descriptors and Overlap Estimation

• URL: http://arxiv.org/abs/2303.06881v3
• Date: Tue, 23 Jul 2024 02:40:36 GMT
• Title: A Coarse-to-Fine Place Recognition Approach using Attention-guided Descriptors and Overlap Estimation
• Authors: Chencan Fu, Lin Li, Jianbiao Mei, Yukai Ma, Linpeng Peng, Xiangrui Zhao, Yong Liu,
• Abstract summary: We present a novel coarse-to-fine approach to place recognition. 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.
• Score: 13.018093610656507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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