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Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2010006(2021)

Visual Odometry Based on Improved Dual-Stream Network Structure

Haidong Zhang, Yiming Xu*, Li Wang, Chunlei Bian, and Fangjie Zhou
Author Affiliations
  • School of Electrical Engineering, Nantong University, Nantong, Jiangsu 226019, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
    Equations (0)
    References (31)
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    Paper Information
    Topics
    Figures & Tables(11)
    System overall structure

    Fig. 1. System overall structure

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    Network framework

    Fig. 2. Network framework

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    Inception structure. (a) Incep1 structure; (b) Incep2 structure

    Fig. 3. Inception structure. (a) Incep1 structure; (b) Incep2 structure

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    CBAM structure diagram

    Fig. 4. CBAM structure diagram

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    Processed data set. (a) RGB image; (b) depth image

    Fig. 5. Processed data set. (a) RGB image; (b) depth image

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    Ground truth processing

    Fig. 6. Ground truth processing

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    Diagram of partly sequence 00--10 trajectory

    Fig. 7. Diagram of partly sequence 00--10 trajectory

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    Image acquisition platform

    Fig. 8. Image acquisition platform

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    Experimental scene and trajectory diagram

    Fig. 9. Experimental scene and trajectory diagram

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    • Table 1. Network layer parameters

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      Table 1. Network layer parameters

      LayerFilter sizeStridePaddingOutput data dimension
      WidthHeightDepth (number of channels)
      Input4161286
      Incep1(Conv1)Multiple (7×7)232086416
      Incep2(Conv2)Multiple (5×5)221043232
      Attention modelMultiple1043232
      Conv33×321521664
      Conv43×321268128
      Conv53×321134256
      Conv63×32172256
      Conv73×32141512
      Flatten2048
      Concat4096
      FC1512
      FC264
      FC36

    • Table 2. Test results of VISO-M, SfMLearner,VISO-S, and proposed method on the KITTI Visual Odometry data set

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      Table 2. Test results of VISO-M, SfMLearner,VISO-S, and proposed method on the KITTI Visual Odometry data set

      SequenceVISO2-MSfMLearnerVISO2-SProposed method
      trmse/%rrmse/[102(°)·m-1]trmse/%rrmse/[102(°)·m-1]trmse/%rrmse/[102(°)·m-1]trmse/%rrmse/[102(°)·m-1]
      0012.053.6723.546.191.860.535.022.11
      0125.557.3959.312.7923.900.312.290.72
      0316.641.3411.974.182.100.423.191.24
      0422.861.6921.133.282.100.271.681.23
      0516.294.5817.144.661.500.502.861.36
      0611.191.9914.875.581.540.331.020.50
      0738.079.2020.986.311.900.932.971.95
      0833.562.4817.413.751.960.605.822.36
      0913.621.7211.114.071.850.463.381.26
      1066.118.0823.304.061.280.534.921.94
      Mean25.594.2122.084.491.790.513.321.47
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    Haidong Zhang, Yiming Xu, Li Wang, Chunlei Bian, Fangjie Zhou. Visual Odometry Based on Improved Dual-Stream Network Structure[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010006

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    Paper Information

    Category: Image Processing

    Received: Nov. 11, 2020

    Accepted: Jan. 2, 2021

    Published Online: Oct. 12, 2021

    The Author Email: Yiming Xu (yimingx@ntu.edu.cn)

    DOI:10.3788/LOP202158.2010006

    Topics

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