Calibration method of liquid lens focusing system for machine vision measurement

Yang Li; Guoming Wang; Ying Wang; Zhi Cheng; Weihu Zhou; Dengfeng Dong

doi:10.3788/IRLA20210472

Journals >Infrared and Laser Engineering >Volume 51 >Issue 6 >Page 20210472 > Article

Infrared and Laser Engineering
Vol. 51, Issue 6, 20210472 (2022)

Calibration method of liquid lens focusing system for machine vision measurement

Yang Li^1、2, Guoming Wang^1、2, Ying Wang^1、2, Zhi Cheng^1、2, Weihu Zhou^1、2、*, and Dengfeng Dong^1、2、*

Author Affiliations

¹Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100094, China

²University of Chinese Academy of Sciences, Beijing 100049, China

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DOI: 10.3788/IRLA20210472 Cite this Article

Yang Li, Guoming Wang, Ying Wang, Zhi Cheng, Weihu Zhou, Dengfeng Dong. Calibration method of liquid lens focusing system for machine vision measurement[J]. Infrared and Laser Engineering, 2022, 51(6): 20210472 Copy Citation Text

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Fig. 1. Optical scheme of liquid lens focusing system and working principle of typical focusing system

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Fig. 2. Structure diagram and electric controlling principle of liquid lens based on elastic membrane cavity

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Fig. 3. Cylindrical mechanical model of liquid lens

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Fig. 4. Relationship between liquid lens' surface vertex offset and deflection angle of chief ray

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Fig. 5. Environmental influence calibration device

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Fig. 6. System parameter calibration devices

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Fig. 7. Test data and fit result of reference temperature equivalent current

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Fig. 8. Calibration result of gravity induced principle point offset

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Fig. 9. Statistical result of positional error using intrinsic parameters acquired by different methods

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Expression of fitting intrinsic parameters	Fitted coefficients
Expression of fitting intrinsic parameters	P₁	P₂	P₃
$ {f_x} = \dfrac{1}{{{P_1} \cdot {I^{(0)}} + {P_2}}} $	−4.366×10−8	1.100×10−4	-
$ {f_y} = \dfrac{1}{{{P_1} \cdot {I^{(0)}} + {P_2}}} $	−4.006×10−8	1.097×10−4	-
$ {v_0} = {P_1} \cdot \Delta {y_{img}} + {P_2} $	68.126	659.556	-
${k_1} = {P_1} \cdot f_{mean(norm)}^2 + {P_2} \cdot {f_{mean(norm)} } + {P_3}$	−1.772×10−2	6.134×10−2	1.367×10−2
${k_2} = {P_1} \cdot f_{mean(norm)}^2 + {P_2} \cdot {f_{mean(norm)} } + {P_3}$	1.198	−2.701	−18.785
${p_1} = {P_1} \cdot f_{mean(norm)}^2 + {P_2} \cdot {f_{mean(norm)} } + {P_3}$	8.530×10−4	−3.357×10−3	1.002×10−2
${p_2} = {P_1} \cdot f_{mean(norm)}^2 + {P_2} \cdot {f_{mean(norm)} } + {P_3}$	−1.060×10−3	8.194×10−4	4.432×10−4

Table 1. Expression of intrinsic parameters and fitted coefficients

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