• Infrared and Laser Engineering
  • Vol. 53, Issue 7, 20240082 (2024)
Changshuai FANG1,2, Zhaoyang LIU3, Qianwen WANG1,2, and Xiaodong ZHANG1,2
Author Affiliations
  • 1State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
  • 2School of Precision Instruments and Optoelectronic Engineering, Tianjin University, Tianjin 300072, China
  • 3Standard Optics technology Tianjin Co., Ltd., Tianjin 300072, China
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    DOI: 10.3788/IRLA20240082 Cite this Article
    Changshuai FANG, Zhaoyang LIU, Qianwen WANG, Xiaodong ZHANG. Low-cost incremental registration method for measuring the surface topography of weak features[J]. Infrared and Laser Engineering, 2024, 53(7): 20240082 Copy Citation Text show less
    Schematic diagram for calculating intersection points and intersection distances
    Fig. 1. Schematic diagram for calculating intersection points and intersection distances
    (a) The top view and (b) the side view of the spatial pose of the measured data after registration to the RPS point and the spatial pose of the model; (c) The top view and (d) the side view of the spatial pose of the measured data after registration to the model point and the spatial pose of the model
    Fig. 2. (a) The top view and (b) the side view of the spatial pose of the measured data after registration to the RPS point and the spatial pose of the model; (c) The top view and (d) the side view of the spatial pose of the measured data after registration to the model point and the spatial pose of the model
    Progressive matching algorithm flowchart
    Fig. 3. Progressive matching algorithm flowchart
    Simulation process
    Fig. 4. Simulation process
    (a) Spatial attitude of the model and the measured data before coarse matching; (b) The difference between the measured data and the model after matching and the difference between the three coordinates and the model
    Fig. 5. (a) Spatial attitude of the model and the measured data before coarse matching; (b) The difference between the measured data and the model after matching and the difference between the three coordinates and the model
    (a) Two deviations after rough matching during simulation; (b) Two deviations after the first fine-tuning during simulation; (c) Two deviations after the second fine-tuning during simulation; (d) The deviation result between the measured data and the three coordinates after the second fine-tuning during simulation
    Fig. 6. (a) Two deviations after rough matching during simulation; (b) Two deviations after the first fine-tuning during simulation; (c) Two deviations after the second fine-tuning during simulation; (d) The deviation result between the measured data and the three coordinates after the second fine-tuning during simulation
    The deviation between the measured data after registration and the CMM (a) without coarse registration steps and (b) without the first step of fine-tuning
    Fig. 7. The deviation between the measured data after registration and the CMM (a) without coarse registration steps and (b) without the first step of fine-tuning
    Data acquisition system
    Fig. 8. Data acquisition system
    (a) Standard steps; (b) Dynamic testing experiment on measurement accuracy of steps at different heights
    Fig. 9. (a) Standard steps; (b) Dynamic testing experiment on measurement accuracy of steps at different heights
    (a) Three-dimensional data for measuring automotive glass; (b) Part CMM points and RPS points
    Fig. 10. (a) Three-dimensional data for measuring automotive glass; (b) Part CMM points and RPS points
    (a) The two deviations after rough matching during actual measurement; (b) The two deviations after the first fine-tuning during actual measurement; (c) The two deviations after the second fine-tuning during actual measurement; (d) The deviation result between the measured data and the three coordinates after the second fine-tuning during actual measurement
    Fig. 11. (a) The two deviations after rough matching during actual measurement; (b) The two deviations after the first fine-tuning during actual measurement; (c) The two deviations after the second fine-tuning during actual measurement; (d) The deviation result between the measured data and the three coordinates after the second fine-tuning during actual measurement
    (a) The deviation between the measurement data directly matched to the coordinate system of the RPS point and the model, as well as the deviation between the three coordinates and the model; The deviation between the measured data after registration and the CMM (b) without coarse registration steps and (c) without the first step of fine-tuning
    Fig. 12. (a) The deviation between the measurement data directly matched to the coordinate system of the RPS point and the model, as well as the deviation between the three coordinates and the model; The deviation between the measured data after registration and the CMM (b) without coarse registration steps and (c) without the first step of fine-tuning
    ParameterValue
    Laser wavelength/nm520
    Focal length/mm12
    Resolution1920×1080
    Stage travel/mm600
    Positioning accuracy/μm10
    Table 1. System parameter
    Changshuai FANG, Zhaoyang LIU, Qianwen WANG, Xiaodong ZHANG. Low-cost incremental registration method for measuring the surface topography of weak features[J]. Infrared and Laser Engineering, 2024, 53(7): 20240082
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