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International Conference on Neural Information Processing

ICONIP 2019: Neural Information Processing pp 450–457Cite as

Combining Fisheye Camera with Odometer for Autonomous Parking

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1142))

Abstract

Simultaneous Localization and Mapping (SLAM) is one of the key technologies for autonomous driving. This paper focuses on the autonomous parking problem. A fisheye camera with a very large field of view is combined with the odometer inside the car to provide the localization information in an underground garage. The odometer provides an initial estimation of pose increment, and then the odometer and camera measurements are jointly optimized by graph optimization. The proposed strategy is evaluated on an autonomous driving platform, and high accuracy is achieved for the trajectory estimation with real scale.

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Acknowledgement

This work was partially financially sponsored by the National Natural Science Foundation of China under grants 61533012, 91748120, and project of Shanghai Automotive Industry Science and Technology Development Foundation.

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Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Donglin Bai & Jianbo Su

Authors
  1. Donglin Bai
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  2. Jianbo Su
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Corresponding author

Correspondence to Jianbo Su .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Bai, D., Su, J. (2019). Combining Fisheye Camera with Odometer for Autonomous Parking. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_49

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  • DOI: https://doi.org/10.1007/978-3-030-36808-1_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36807-4

  • Online ISBN: 978-3-030-36808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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