A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional...

Area of Science:

• Robotics
• Computer Vision
• Metrology

Background:

• Accurate 3D measurement is crucial for industrial applications.
• Structured light vision sensors on robotic arms require precise calibration.
• Existing calibration methods may not achieve desired accuracy levels.

Purpose of the Study:

• To propose a novel sensor calibration approach for robot-based 3D measurement systems.
• To enhance the measurement accuracy of structured light vision sensors.
• To develop a hybrid camera model combining geometric and neural network approaches.

Main Methods:

• Utilizing a calibration target with fixed concentric circles to determine projected centers.
• Employing a calibrated industrial robot for generating calibration points.
• Applying the radial alignment constraint (RAC) method for camera model calibration.
• Using a multilayer perceptron neural network (MLPNN) to refine calibration residuals.

Main Results:

• The novel calibration approach significantly improves the accuracy of the structured light vision sensor model.
• Experimental validation using a standard ball confirms the effectiveness of the technique.
• The hybrid pinhole model and MLPNN accurately represent the real camera model.

Conclusions:

• The proposed calibration method achieves highly accurate 3D measurement models for robotic vision systems.
• This technique offers a robust solution for enhancing precision in industrial metrology.
• The integration of concentric circles, RAC, and MLPNN provides a powerful calibration framework.