• Optical Instruments
  • Vol. 41, Issue 6, 79 (2019)
Xiaoxu XU, Yingping HUANG*, and Xing HU
Author Affiliations
  • School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • show less
    DOI: 10.3969/j.issn.1005-5630.2019.06.013 Cite this Article
    Xiaoxu XU, Yingping HUANG, Xing HU. Calibration of lidar-camera fusion system for intelligent vehicles[J]. Optical Instruments, 2019, 41(6): 79 Copy Citation Text show less
    Diagram of related coordinate systems
    Fig. 1. Diagram of related coordinate systems
    Calibration template of feature-point method
    Fig. 2. Calibration template of feature-point method
    Principle diagram of planar checkerboard method
    Fig. 3. Principle diagram of planar checkerboard method
    Installation diagram of the lidar-camera system
    Fig. 4. Installation diagram of the lidar-camera system
    Diagram of validation experiment
    Fig. 5. Diagram of validation experiment
    Projection of lidar points to image
    Fig. 6. Projection of lidar points to image
    激光雷达特征点/m图像特征点/pixel求得的矩阵A
    [0.01,−0.12,8.49][285,314]
    [0.45,−0.12,8.48][325,313]
    [0.83,−0.14,10.15][346,293]
    [1.22,−0.14,10.15][374,292]
    [0.77,−0.16,11.87][334,278]
    [1.15,−0.16,11.87][357,278]
    Table 1. Extracted feature points and calculated parameters
    i个位置 Pi,1/m Pi,2/m Pi,3/m NL/m NC/m
    1[−0.48,−0.20,7.35][−0.40,−0.10,7.43][−0.35,0.20,7.36][038,−0.08,−7.36][−0.67,0.51,8.77]
    2[−0.20,−0.14,5.10][−0.11,−0.14,5.05][0.02,−0.14,5.02][1.78,0.71,4.09][1.97,0.37,5.22]
    3[−0.39,−0.10,7.78][−0.20,−0.21,7.68][0.06,−0.21,7.68][1.13,1.02,7.31][0.83,0.68,8.53]
    Table 2. NL, NC of the checkerboard planes at different positions and Pl at corresponding positions
    结果RUnknown environment 'document'Unknown environment 'document'内部参数/像素
    初始解
    优化解
    Table 3. Results of the parameters
    靶点 编号 激光雷达 坐标/m 实际图像坐标 [ua,iva,i]/像素 特征点法投影结果 [uc,ivc,i]/像素 特征点法对准 误差/像素 棋盘格法投影结果 [uc,ivc,i]/像素 棋盘格法对准 误差/像素
    1[0.73,−0.28,9.93][346,285][346.60,287.24]2.31[349.46,284.50]3.49
    2[1.06,−0.29,9.93][371,285][373.14,282.68]3.15[373.84,283.33]3.29
    3[1.39,−0.26,9.88][399,287][397.94,284.35]2.85[398.69,285.50]1.53
    4[−2.76,−0.38,17.79][172,248][168.78,245.39]4.14[170.56,246.85]1.84
    5[−2.43,−0.33,17.80][187,248][184.66,247.52]2.38[185.11,248.77]2.04
    6[−2.05,−0.30, 17.77][202,251][201.62,249.78]1.27[201.61,249.90]1.16
    7[−0.20,−0.41,19.93][284,241][281.26,238.52]3.69[282.31,239.25]2.43
    8[0.19,−0.41,19.92][299,241][296.81,238.41]3.39[297.44,239.04]2.50
    9[0.60,−0.41, 19.89][315,241][312.24,237.97]4.09[313.37,238.86]2.69
    平均值3.032.33
    Table 4. Results of the projection of the two methods
    Xiaoxu XU, Yingping HUANG, Xing HU. Calibration of lidar-camera fusion system for intelligent vehicles[J]. Optical Instruments, 2019, 41(6): 79
    Download Citation