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MAIM-VO: A Robust Visual Odometry with Mixed MLP for Weak Textured Environment

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Image and Graphics Technologies and Applications (IGTA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1910))

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Abstract

Visual localization is a critical technology for visual SLAM systems, which determines the relative position and motion trajectory by tracking feature points. In recent years, deep learning has been widely applied to the field of visual localization. The method based on deep learning is capable of surpassing the limitations of traditional manual feature extraction methods and achieving high-precision visual localization in complex scenes, thus realizing the goal of lifelong SLAM. The MLP model has characteristics such as flexibility and adaptability. The Mixer-WMLP achieves token information exchange between spatial positions by evenly dividing the feature map into non-overlapping windows, which makes the Mixer-WMLP approach a global receptive field. Compared to CNNs and Transformers, Mixer MLPs have higher computational efficiency and robustness. In this paper, we utilize the Mixer MLP structure to design a deep learning-based visual odometry system called MAIM-VO. Even in complex scenes with low texture areas, high-quality matching can be achieved. After obtaining the matching point pairs, the camera pose is solved in an optimized way by minimizing the reprojection error of the feature points. Multiple datasets and experiments in real-world environments have demonstrated that MIAM-VO exhibits higher robustness and relative localization accuracy compared to currently popular visual SLAM systems.

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Acknowledgments

This work was supported by the Institute of Robotics and Intelligent Manufacturing Innovation, Chinese Academy of Sciences (Grant number: C2021002).

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Authors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China

    Zhiwei Shen & Bin Kong

  2. University of Science and Technology of China, Hefei, 230026, China

    Zhiwei Shen

  3. Anhui Engineering Laboratory for Intelligent Driving Technology and Application, Hefei, 230088, China

    Bin Kong

  4. Innovation Research Institute of Robotics and Intelligent Manufacturing (Hefei), Chinese Academy of Sciences, Hefei, 230088, China

    Bin Kong

Authors
  1. Zhiwei Shen
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  2. Bin Kong
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Corresponding authors

Correspondence to Zhiwei Shen or Bin Kong .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Wang Yongtian

  2. Beijing University of Technology, Beijing, China

    Wu Lifang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Shen, Z., Kong, B. (2023). MAIM-VO: A Robust Visual Odometry with Mixed MLP for Weak Textured Environment. In: Yongtian, W., Lifang, W. (eds) Image and Graphics Technologies and Applications. IGTA 2023. Communications in Computer and Information Science, vol 1910. Springer, Singapore. https://doi.org/10.1007/978-981-99-7549-5_6

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  • DOI: https://doi.org/10.1007/978-981-99-7549-5_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7548-8

  • Online ISBN: 978-981-99-7549-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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