Binocular Camera Calibration Using 3D Distortion Mapping In-Line
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Solution Overview
Problem
Existing binocular camera calibration methods require stopping the production line, leading to efficiency losses and reduced accuracy due to vibration-induced errors.
Innovation Solution
An intelligent manufacturing industrial Internet of Things system with a front split service platform that performs on-site calibration of binocular cameras by detecting distortion in three-dimensional image data, mapping judgment points, and calibrating cameras without stopping the production line, using a recognition module and calibration module to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional binocular camera calibration method using checkerboard is employed, then calibration accuracy can be improved, but production line must be stopped resulting in reduced productivity
Solution Approach 1:
The system performs preliminary calibration actions continuously in the background during normal production operations. By pre-calculating calibration values and maintaining real-time monitoring of camera parameters, the system prepares calibration data beforehand so that when calibration is needed, it can be applied without stopping the production line, thus maintaining both accuracy and productivity
Solution Approach 2:
The calibration system serves itself by automatically detecting distortion in three-dimensional image data, calculating calibration values, and updating camera parameters without requiring external intervention or production line shutdown. The system uses its own operational data to perform self-calibration, eliminating the need for manual checkerboard calibration that would halt production
2Manufacturing precision
If binocular camera is used to obtain 3D images with depth of field, then imaging quality is improved, but vibration-induced errors reduce measurement precision
Solution Approach 1:
The system implements a feedback mechanism where distortion detection results from three-dimensional image data are continuously monitored and used to adjust calibration values. By detecting distortion patterns and feeding this information back to the calibration module, the system can compensate for vibration-induced errors in real-time, maintaining measurement precision despite production line vibrations
Solution Approach 2:
The system dynamically changes calibration parameters based on detected distortion levels and production conditions. By adjusting calibration values according to real-time distortion detection, the system adapts to varying vibration conditions and maintains accurate 3D imaging quality throughout production operations
Data Source
AI summary
An intelligent manufacturing industrial Internet of Things with a front split service platform for calibrating a binocular camera is provided, which includes: an obtaining module configured to obtain an image taken by a first camera on a production line as a first image data and an image taken by a second camera as a second image data through a sensor network platform; a three-dimensional module configured to process into three-dimensional image data; a recognition module configured to obtain a plurality of point positions of a distortion part as judgment point positions; a mapping module configured to map the judgment point positions to the second image data to form second calibration point positions; and a calibration module configured to calibrate the first camera and the second camera according to first calibration point positions and the second calibration point positions.