Fig. 1. Flowchart of the proposed registration method
Fig. 2. Schematic illustration of the junction structure
Fig. 3. Projection geometry of the junction structure in two-views
Fig. 4. Automatic detection of conjugate LiDAR plane points
Fig. 5. Registration model under constraints of junction structure features
Fig. 6. Overview of the image data and LiDAR point cloud for the Guangzhou data set
Fig. 7. Overview of check points for the Ningbo data set
Fig. 8. Measurements of image junction structures for the Guangzhou data set
Fig. 9. LiDAR point cloud colored by aerial images for the Guangzhou data set
Fig. 10. Comparison of the fitness of LiDAR points to images before and after registration for the Guangzhou data
Fig. 11. Comparison of the fitness of LiDAR points to images before and after registration for the Ningbo data
Fig. 12. Comparison of registration accuracy using thinning point clouds for the Guangzhou data set
Fig. 13. Comparison of registration accuracy using thinning point clouds for the Ningbo data set
数据集 | Ⅰ | Ⅱ |
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航空影像 | 测区位置 | 广州 | 宁波 | 航高/m | 500 | 900 | 地面分辨率/m | 0.032 | 0.048 | 像幅大小/pixels | 10 336×7788 | 11 608×8708 | 相机数 | 5 | 5 | | 影像总数 | 2415 | 1451 | LiDAR | 点云密度/(pts/m2) | 16 | 10 | 点云间距/m | 0.25 | 0.30 | 总点数 | 43 971 092 | 45 154 384 |
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Table 1. Details of the experimental data
类别 | 角特征配准方法/m | | 交叉点配准方法/m |
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dXY | dZ | dXY | dZ |
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残差中误差 | 0.052 | 0.101 | | 0.042 | 0.058 | 残差平均值 | 0.035 | 0.069 | | 0.038 | 0.054 | 残差最大值 | 0.126 | 0.249 | | 0.116 | 0.097 |
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Table 2. Comparison of registration accuracy for the Guangzhou data set
类别 | 角特征配准方法/m | | 交叉点配准方法/m |
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dX | dY | dXY | dZ | dX | dY | dXY | dZ |
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残差中误差 | 0.076 | 0.063 | 0.109 | 0.100 | | 0.051 | 0.025 | 0.057 | 0.063 | 残差平均值 | -0.031 | 0.002 | 0.089 | 0.062 | 0.045 | 0.003 | 0.051 | -0.044 | 残差最大值 | -0.176 | -0.141 | 0.183 | 0.296 | 0.096 | -0.054 | 0.099 | -0.112 |
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Table 3. Comparison of check points residuals for the Ningbo data set