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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2303.06881 |
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| _version_ | 1866914881878360064 |
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| author | Fu, Chencan Li, Lin Mei, Jianbiao Ma, Yukai Peng, Linpeng Zhao, Xiangrui Liu, Yong |
| author_facet | Fu, Chencan Li, Lin Mei, Jianbiao Ma, Yukai Peng, Linpeng Zhao, Xiangrui Liu, Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_06881 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Coarse-to-Fine Place Recognition Approach using Attention-guided Descriptors and Overlap Estimation Fu, Chencan Li, Lin Mei, Jianbiao Ma, Yukai Peng, Linpeng Zhao, Xiangrui Liu, Yong Computer Vision and Pattern Recognition Robotics 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. |
| title | A Coarse-to-Fine Place Recognition Approach using Attention-guided Descriptors and Overlap Estimation |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2303.06881 |