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Acta Optica Sinica, Volume. 38, Issue 4, 0411001(2018)

IEPnP: An Iterative Camera Pose Estimation Algorithm Based on EPnP

Peng Chen* and Chenxiao Wang
Author Affiliations
  • College of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
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    References (18)
    Cited By (9)
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    Figures & Tables(10)
    Coordinate systems involved in the imaging model

    Fig. 1. Coordinate systems involved in the imaging model

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    Orthogonal projection model

    Fig. 2. Orthogonal projection model

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    FTF eigenvalues under different focal lengths. (a) Varying curve of eigenvalues of FTF; (b) varying curve of the first 6 eigenvalues of FTF

    Fig. 3. FTF eigenvalues under different focal lengths. (a) Varying curve of eigenvalues of FTF; (b) varying curve of the first 6 eigenvalues of FTF

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    Curves of the image noise when the variance is 0. (a) Attitude estimation curve; (b) translation estimation curve

    Fig. 4. Curves of the image noise when the variance is 0. (a) Attitude estimation curve; (b) translation estimation curve

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    Curves of the image noise when the variance is 0. (a) Attitude estimation error; (b) translation estimation error

    Fig. 5. Curves of the image noise when the variance is 0. (a) Attitude estimation error; (b) translation estimation error

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    Curves of the image noise when the variance is 4. (a) Attitude estimation curve; (b) translation estimation curve

    Fig. 6. Curves of the image noise when the variance is 4. (a) Attitude estimation curve; (b) translation estimation curve

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    Curves of the image noise when the variance is 4. (a) Attitude estimation error; (b) translation estimation error

    Fig. 7. Curves of the image noise when the variance is 4. (a) Attitude estimation error; (b) translation estimation error

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    Comparison of the running time of the algorithm. (a) Image noise variance is 0; (b) image noise variance is 4

    Fig. 8. Comparison of the running time of the algorithm. (a) Image noise variance is 0; (b) image noise variance is 4

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    • Table 1. Pose estimation error comparison when the image noise variance is 0

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      Table 1. Pose estimation error comparison when the image noise variance is 0

      AlgorithmMean(Erot) /10-5Std(Erot) /10-5Median(Erot) /10-6Mean(Etrans)Std(Etrans)Median(Etrans)
      EPnP+GN1.21211.69046.35690.00249310.00175290.0023812
      IEPnP1.21211.69046.35690.00249310.00175290.0023812

    • Table 2. Pose estimation error comparison when the image noise variance is 4

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      Table 2. Pose estimation error comparison when the image noise variance is 4

      AlgorithmMean(Erot)Std(Erot)Median(Erot)Mean(Etrans)Std(Etrans)Median(Etrans)
      EPnP+GN0.0300480.18671000.000270940.0636220.3124400.012155
      IEPnP0.00069340.00160830.000250740.0142240.0114480.011982
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    Peng Chen, Chenxiao Wang. IEPnP: An Iterative Camera Pose Estimation Algorithm Based on EPnP[J]. Acta Optica Sinica, 2018, 38(4): 0411001

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    Paper Information

    Category: Imaging Systems

    Received: Sep. 26, 2017

    Accepted: --

    Published Online: Jul. 10, 2018

    The Author Email: Chen Peng (holmes83@163.com)

    DOI:10.3788/AOS201838.0411001

    Topics

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