Biography

I have recived my bachelor’s degree in Mechatronic Engineering, Harbin Institute of Technology, Harbin, China and I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include sensor fusion and SLAM.

Research and Interests

• sensor fusion
• SLAM

Publications

• Chencan Fu, Lin Li, Jianbiao Mei, Yukai Ma, Linpeng Peng, Xiangrui Zhao, and Yong Liu. A Coarse-to-Fine Place Recognition Approach using Attention-guided Descriptors and Overlap Estimation. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 8493-8499, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  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.

  @inproceedings{fu2024ctf,
  title = {A Coarse-to-Fine Place Recognition Approach using Attention-guided Descriptors and Overlap Estimation},
  author = {Chencan Fu and Lin Li and Jianbiao Mei and Yukai Ma and Linpeng Peng and Xiangrui Zhao and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {8493-8499},
  doi = {10.1109/ICRA57147.2024.10611569},
  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.}
  }