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Stable Monocular Visual Odometry Based on Optical Flow Matching

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Computer Networks and IoT (IAIC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2060))

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Abstract

A monocular visual odometry method combining deep learning and geometry is proposed to address the sparsity and position discontinuity problems. An unsupervised optical flow estimation network is constructed to obtain high-quality dense optical flow. Next, optical flow masks and depth masks are proposed for filtering key points. Finally, position estimation and scale recovery are performed based on multi-view geometry. Experiments are extensively validated on the KITTI dataset, and the visual odometry method achieves 3.48 (%) translation error and 0.67 rotation error (\(^{\circ }\)/100 m) outperforming the baseline DF-VO.

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Author information

Authors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, 210094, China

    Yujia Liu & Chaoxia Shi

  2. Nanjing Xiaozhuang University, Nanjing, 211171, China

    Yanqing Wang

Authors
  1. Yujia Liu
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  2. Chaoxia Shi
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  3. Yanqing Wang
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Corresponding authors

Correspondence to Chaoxia Shi or Yanqing Wang .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, Hubei, China

    Hai Jin

  2. Chinese Academy of Science, Shenzhen, China

    Yi Pan

  3. Nanjing University of Science and Technology, Nanjing, China

    Jianfeng Lu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Liu, Y., Shi, C., Wang, Y. (2024). Stable Monocular Visual Odometry Based on Optical Flow Matching. In: Jin, H., Pan, Y., Lu, J. (eds) Computer Networks and IoT. IAIC 2023. Communications in Computer and Information Science, vol 2060. Springer, Singapore. https://doi.org/10.1007/978-981-97-1332-5_2

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  • DOI: https://doi.org/10.1007/978-981-97-1332-5_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1331-8

  • Online ISBN: 978-981-97-1332-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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