A Coarse-to-Fine Place Recognition Approach using Attention-guided Descriptors and Overlap Estimation
This addresses the problem of efficient and accurate place recognition for robotics, though it appears incremental as it builds on existing methods.
The paper tackles place recognition in robotics by proposing a coarse-to-fine approach that combines attention-guided descriptors and overlap estimation, achieving state-of-the-art performance on KITTI and KITTI-360 datasets.
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.