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Laser & Optoelectronics Progress, Volume. 58, Issue 12, 1215006(2021)

Research on SLAM Loop Closure Detection Method Based on HHO Algorithm

Cuijun Zhang1,2 and Yuhe Zhang1、*
Author Affiliations
  • 1College of Information Engineering, Hebei GEO University, Shijiazhuang, Hebei 0 50031, China
  • 2Hebei Center for Ecological and Environmental Geology Research, Hebei GEO University, Shijiazhuang, Hebei 0 50031, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
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    References (26)
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    Figures & Tables(14)
    Contrast charts of feature point detection of improved FAST algorithm. (a) δ=0.1; (b) δ=0.2; (c) δ=0.3; (d) δ=0.4; (e) δ=0.5

    Fig. 1. Contrast charts of feature point detection of improved FAST algorithm. (a) δ=0.1; (b) δ=0.2; (c) δ=0.3; (d) δ=0.4; (e) δ=0.5

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    Comparison of feature extraction effect before and after FAST algorithm improvement.(a) Before improvement; (b) after improvement

    Fig. 2. Comparison of feature extraction effect before and after FAST algorithm improvement.(a) Before improvement; (b) after improvement

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    Comparison of detection results before and after FAST algorithm improvement when image brightness is reduced by 50%. (a) Before improvement; (b) after improvement

    Fig. 3. Comparison of detection results before and after FAST algorithm improvement when image brightness is reduced by 50%. (a) Before improvement; (b) after improvement

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    Comparison of detection results before and after FAST algorithm improvement when image brightness is doubled. (a) Before improvement; (b) after improvement

    Fig. 4. Comparison of detection results before and after FAST algorithm improvement when image brightness is doubled. (a) Before improvement; (b) after improvement

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    Feature extraction comparison of the original algorithm. (a) Original image; (b) brightness is reduced by 50%; (c) brightness is doubled

    Fig. 5. Feature extraction comparison of the original algorithm. (a) Original image; (b) brightness is reduced by 50%; (c) brightness is doubled

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    Feature extraction comparison of the improved FAST algorithm. (a) Original image; (b) brightness is reduced by 50%; (c) brightness is doubled

    Fig. 6. Feature extraction comparison of the improved FAST algorithm. (a) Original image; (b) brightness is reduced by 50%; (c) brightness is doubled

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    Steps of loop closure detection method based on HHO algorithm

    Fig. 7. Steps of loop closure detection method based on HHO algorithm

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    P-R curves of three loop closure detection methods. (a) On KITTI dataset; (b) on freiburg2_desk dataset

    Fig. 8. P-R curves of three loop closure detection methods. (a) On KITTI dataset; (b) on freiburg2_desk dataset

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    Fitness change curves on freiburg2_desk dataset. (a) Current frame number is 183; (b) current frame number is 2839

    Fig. 9. Fitness change curves on freiburg2_desk dataset. (a) Current frame number is 183; (b) current frame number is 2839

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    Fitness change curves on KITTI dataset. (a) Current frame number is 625; (b) current frame number is 3633

    Fig. 10. Fitness change curves on KITTI dataset. (a) Current frame number is 625; (b) current frame number is 3633

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    Time comparison of three loop closure detection methods on freiburg2_desk dataset. (a) Current frame number is 183; (b) current frame number is 2839

    Fig. 11. Time comparison of three loop closure detection methods on freiburg2_desk dataset. (a) Current frame number is 183; (b) current frame number is 2839

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    Time comparison of three loop closure detection methods on KITTI dataset. (a) Current frame number is 625; (b) current frame number is 3633

    Fig. 12. Time comparison of three loop closure detection methods on KITTI dataset. (a) Current frame number is 625; (b) current frame number is 3633

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    • Table 1. Four different location update strategies in the exploitation phase

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      Table 1. Four different location update strategies in the exploitation phase

      Value of r and |E|Strategy
      r≥0.5 and |E|≥0.5Soft besiege
      r≥0.5 and |E|<0.5Hard besiege
      r<0.5 and |E|≥0.5Soft besiege with progressive rapid dives
      r<0.5 and |E|<0.5Hard besiege with progressive rapid dives

    • Table 2. Number of feature points detected by improved FAST algorithm when setting different δ

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      Table 2. Number of feature points detected by improved FAST algorithm when setting different δ

      Parameterδ
      0.10.20.30.40.5
      Number of feature points419018611037610384
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    Cuijun Zhang, Yuhe Zhang. Research on SLAM Loop Closure Detection Method Based on HHO Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1215006

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    Paper Information

    Category: Machine Vision

    Received: Aug. 24, 2020

    Accepted: Nov. 14, 2020

    Published Online: Jun. 23, 2021

    The Author Email: Zhang Yuhe (862000954@qq.com)

    DOI:10.3788/LOP202158.1215006

    Topics

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