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Laser & Optoelectronics Progress, Volume. 61, Issue 4, 0411005(2024)

Camera-IMU State Estimation and External Parameter Online Calibration Algorithm

Zinian Mao1, Zhifeng Zhou1、*, Yichun Shen2, and Liduan Wang3
Author Affiliations
  • 1School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
  • 2Shanghai Satellite Engineering Research Institute, Shanghai 200240, China
  • 3Shanghai Si Nan Satellite Navigation Technology Co., Ltd., Shanghai 201801, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (18)
    References (24)
    Cited By (1)
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    Figures & Tables(12)
    Schematic diagram of coordinate system transformation

    Fig. 1. Schematic diagram of coordinate system transformation

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    Schematic diagram of sliding window model

    Fig. 2. Schematic diagram of sliding window model

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    Flow chart of Camera-IMU external parameter calibration framework

    Fig. 3. Flow chart of Camera-IMU external parameter calibration framework

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    Schematic diagram of rotation increment calibration model

    Fig. 4. Schematic diagram of rotation increment calibration model

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    Odometer incremental angle

    Fig. 5. Odometer incremental angle

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    Image RTK experimental platform. (a) Experimental platform structure; (b) schematic diagram of the coordinate system

    Fig. 6. Image RTK experimental platform. (a) Experimental platform structure; (b) schematic diagram of the coordinate system

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    Rotation external parameter convergence process. (a) Yaw; (b) pitch; (c) roll

    Fig. 7. Rotation external parameter convergence process. (a) Yaw; (b) pitch; (c) roll

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    Translation external parameter convergence process. (a) x-axis; (b) y-axis; (c) z-axis

    Fig. 8. Translation external parameter convergence process. (a) x-axis; (b) y-axis; (c) z-axis

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    Track visualization. (a) Trajectory of proposed algorithm; (b) trajectory of traditional algorithm

    Fig. 9. Track visualization. (a) Trajectory of proposed algorithm; (b) trajectory of traditional algorithm

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    Evaluation parameters of APE

    Fig. 10. Evaluation parameters of APE

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    • Table 1. Rotation external parameter calibration error in different threshold weighting methods

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      Table 1. Rotation external parameter calibration error in different threshold weighting methods

      ThresholdWeighting methodErrorMean error
      YawPitchRoll
      1σ3/σ42.9333.2321.2242.463
      1Average3.3762.2341.8932.501
      2σ3/σ41.3780.9871.6031.323
      2Average3.7383.4791.6732.963
      3σ3/σ42.3900.6742.7861.950
      3Average4.3791.0233.3562.919

    • Table 2. External parameter measurement and convergence time

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      Table 2. External parameter measurement and convergence time

      ParameterRotate the outer parametersPan the outer parameter
      Yaw /（°）Pitch /（°）Roll /（°）x /cmy /cmz /cm
      Convergence time /s222320203531
      True value00806.82.57.2
      Calibration value－1.81.778.37.63.36.3
      Error1.81.71.70.80.80.9
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    Zinian Mao, Zhifeng Zhou, Yichun Shen, Liduan Wang. Camera-IMU State Estimation and External Parameter Online Calibration Algorithm[J]. Laser & Optoelectronics Progress, 2024, 61(4): 0411005

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    Paper Information

    Category: Imaging Systems

    Received: Apr. 27, 2023

    Accepted: Jun. 5, 2023

    Published Online: Feb. 6, 2024

    The Author Email: Zhifeng Zhou (zhousjtu@126.com)

    DOI:10.3788/LOP231200

    CSTR:32186.14.LOP231200

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology