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Loop Closure Detection Based on Local and Global Descriptors with Sinkhorn Algorithm

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6GN for Future Wireless Networks (6GN 2023)

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Abstract

This paper presents a novel loop closure detection pipeline for SLAM systems, addressing the limitations of current deep learning methods in maintaining 3D point cloud structure and extracting high-quality semantic features. We utilize U-Net and FPN for feature extraction, with a descriptor generator that learns from local descriptors. The Sinkhorn algorithm is incorporated for 6DOF transformation matching between point clouds, effectively managing occlusions and aligning source and target clouds. Our method, evaluated on the KITTI dataset, outperforms traditional and other deep learning methods in computational efficiency and real-time performance.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (grant 61802247).

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Authors and Affiliations

  1. School of Electronic and Information, Shanghai Dianji University, Shanghai, China

    Wei Xiao & Dong Zhu

Authors
  1. Wei Xiao
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  2. Dong Zhu
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Correspondence to Wei Xiao .

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Editors and Affiliations

  1. Shanghai Dianji University, Shanghai, China

    Jingchao Li

  2. Kanagawa University, Yokohama, Japan

    Bin Zhang

  3. Shanghai University of Electric Power, Yangpu, China

    Yulong Ying

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Xiao, W., Zhu, D. (2024). Loop Closure Detection Based on Local and Global Descriptors with Sinkhorn Algorithm. In: Li, J., Zhang, B., Ying, Y. (eds) 6GN for Future Wireless Networks. 6GN 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-031-53401-0_29

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  • DOI: https://doi.org/10.1007/978-3-031-53401-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53400-3

  • Online ISBN: 978-3-031-53401-0

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