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Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

PRCV 2019: Pattern Recognition and Computer Vision pp 172–183Cite as

Rotational Alignment of IMU-camera Systems with 1-Point RANSAC

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11859))

Abstract

In this paper we present a minimal solution for the rotational alignment of IMU-camera systems based on a homography formulation. The image correspondences between two views are related by homography when the motion of the camera can be effectively approximated as a pure rotation. By exploiting the rotational angles of the features obtained by e.g. the SIFT detector, we compute the rotational alignment of IMU-camera systems with only 1 feature correspondence. The novel minimal case solution allows us to cope with feature mismatches efficiently and robustly within a random sample consensus (RANSAC) scheme. Our method is evaluated on both synthetic and real scene data, demonstrating that our method is suited for the rotational alignment of IMU-camera systems.

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Notes

  1. 1.

    k-quantiles divide an ordered data set into k regular intervals.

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Author information

Authors and Affiliations

  1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China

    Banglei Guan, Ang Su & Zhang Li

  2. Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha, China

    Banglei Guan, Ang Su & Zhang Li

  3. Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria

    Friedrich Fraundorfer

Authors
  1. Banglei Guan
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  2. Ang Su
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  3. Zhang Li
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  4. Friedrich Fraundorfer
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Corresponding author

Correspondence to Ang Su .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

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

    Jian Yang

  4. Xidian University, Xi’an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi’an, China

    Yanning Zhang

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Guan, B., Su, A., Li, Z., Fraundorfer, F. (2019). Rotational Alignment of IMU-camera Systems with 1-Point RANSAC. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-31726-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31725-6

  • Online ISBN: 978-3-030-31726-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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