• Infrared and Laser Engineering
  • Vol. 52, Issue 3, 20220618 (2023)
Ronghua Li1, Meng Wang1, Wei Zhou2, and Jiaru Fu1
Author Affiliations
  • 1Institute of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China
  • 2No.91550 Unit of the PLA, Dalian 116023, China
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    DOI: 10.3788/IRLA20220618 Cite this Article
    Ronghua Li, Meng Wang, Wei Zhou, Jiaru Fu. Pose estimation of flying target based on bi-modal information fusion[J]. Infrared and Laser Engineering, 2023, 52(3): 20220618 Copy Citation Text show less
    Flow chart of bimodal fusion algorithm
    Fig. 1. Flow chart of bimodal fusion algorithm
    Point cloud and image fusion
    Fig. 2. Point cloud and image fusion
    Point cloud densification
    Fig. 3. Point cloud densification
    Moving object extraction
    Fig. 4. Moving object extraction
    Image morphological processing
    Fig. 5. Image morphological processing
    Target point cloud extraction
    Fig. 6. Target point cloud extraction
    Feature line extraction
    Fig. 7. Feature line extraction
    Point cloud model coordinate system
    Fig. 8. Point cloud model coordinate system
    Pose solution results
    Fig. 9. Pose solution results
    Physical object and point cloud model
    Fig. 10. Physical object and point cloud model
    Simulation data
    Fig. 11. Simulation data
    Display of pose calculation process in frame 20
    Fig. 12. Display of pose calculation process in frame 20
    Display of pose calculation process in frame 30
    Fig. 13. Display of pose calculation process in frame 30
    Pose solution error
    Fig. 14. Pose solution error
    ICP algorithm error
    Fig. 15. ICP algorithm error
    Two scene color pictures
    Fig. 16. Two scene color pictures
    Comparison of image target extraction algorithms
    Fig. 17. Comparison of image target extraction algorithms
    Calculation of target pose in different scenes
    Fig. 18. Calculation of target pose in different scenes
    NameValue
    ModelRS-LD1605 M
    Horizontal field of view/(°)56
    Vertical field of view/(°)31
    Color image resolution2448×1378
    Depth image resolution640×360
    Horizontal angle resolution/(°)0.088
    Vertical angle resolution/(°)0.088
    Detection distance/m50
    Detection accuracy/cm5
    Frame rate/Hz12
    Table 1. Parameter range of RS-LD1605 M radar
    GroupΔPx/mm ΔPy/mm ΔPz/mm Δβ/(°) Δγ/(°) Time/ms
    Proposed algorithm1.064.592.070.631.01132
    ICP algorithm2.938.374.210.941.62261
    PnP algorithm1.655.454.110.761.3337
    Algorithm in Ref.[10] 2.516.023.880.821.41327
    Table 2. Comparison of algorithm accuracy
    SceneGroupTPFPTNFNPRPWC
    Scene AOur method1 52040336 968 42800.9620.8440.009 4%
    Background subtraction1 6276 015336 372 91730.2130.9040.183%
    Vibe algorithm1 642178336 956 61580.9020.9120.009 9%
    Algorithm in Ref. [5] 1 732117336 962 7680.9360.9620.005 5%
    Scene BOur method1 60731336 931 33930.9780.8030.013%
    Background subtraction1 6277 302336 204 23730.1820.8130.228%
    Vibe algorithm1 766725336 861 92340.7090.8830.028%
    Algorithm in Ref. [5] 1 9391 007336 833 7610.6580.9690.032%
    Table 3. Comparison of target extraction accuracy of image
    GroupA/msB/ms
    Proposed algorithm301289
    Background subtraction153147
    Vibe algorithm271253
    Algorithm in Ref.[5] 206198
    Table 4. Time consuming for image target extraction
    Scene numberΔPx/mm ΔPy/mm ΔPz/mm Δβ/(°) Δγ/(°)
    Scene A2.7015−0.414910.277843.681366.7326
    Scene B1.33680.172310.503682.83294.6933
    Table 5. Calculation results of target pose
    Scene numberΔPx/mm ΔPy/mm ΔPz/mm Δβ/(°) Δγ/(°)
    Scene A2.7073−0.421310.281844.351865.3863
    Scene B1.33280.168510.507281.75675.3052
    Scene A2.7062−0.413710.274744.272165.8147
    Scene B1.33170.170510.509381.26025.3872
    Table 6. PnP pose calculation
    Scene numberΔPx/mm ΔPy/mm ΔPz/mm Δβ/(°) Δγ/(°)
    Scene A5.252.64.50.63−1.13
    Scene B−4.55−2.84.65−1.320.65
    Table 7. Position and attitude error comparison
    ColourWhiteBlueRed
    Acquisition rate10.970.94
    Table 8. Point cloud collection rate under different surface colors
    GroupΔPx/mm ΔPy/mm ΔPz/mm Δβ/(°) Δγ/(°)
    Group A0.974.261.840.580.97
    Group B0.0610.0620.0390.0140.082
    Our algorithm1.064.592.070.631.01
    Table 9. Number of point clouds at different object distances and surface colors
    GroupΔPx/mm ΔPy/mm ΔPz/mm
    Group A0.974.261.84
    Group B0.0610.0620.039
    Our method1.064.592.07
    Table 10. Position error
    GroupΔβ/(°) Δγ/(°)
    Group A0.580.97
    Group B0.0140.082
    Our method0.631.01
    Table 11. Angle error
    Ronghua Li, Meng Wang, Wei Zhou, Jiaru Fu. Pose estimation of flying target based on bi-modal information fusion[J]. Infrared and Laser Engineering, 2023, 52(3): 20220618
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