Multi-Camera Calibration Assessment via 3D Reconstruction
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Solution Overview
Problem
Current calibration quality checks for multiple camera systems are laborious and time-consuming, requiring extensive use of calibration boards and processing time due to high image resolution and numerous poses, which limits efficiency in environments like aircraft manufacturing.
Innovation Solution
A method and system that automatically assesses camera calibration by extracting features from captured images using ORB, SURF, and ANMS, matching features through brute force or FLANN, and generating a three-dimensional reconstruction to indicate calibration errors, reducing the need for manual calibration boards and speeding up the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration quality check procedures are used, then calibration accuracy can be assessed, but the process is laborious and time-consuming requiring positioning calibration boards at numerous locations and orientations
Solution Approach 1:
The patent extracts the essential calibration assessment function from the complex conventional procedure by using feature matching between camera pairs. Instead of requiring full calibration boards and procedures, the system extracts calibration quality information directly from natural or projected features in the scene, eliminating the need for extensive calibration board positioning while maintaining assessment accuracy
Solution Approach 2:
The system creates a three-dimensional reconstruction model that copies the spatial relationships and calibration information from the camera array. This digital model allows calibration assessment without physically repositioning calibration boards, as the 3D reconstruction inherently encodes the calibration quality through feature matching consistency across multiple camera views
2Reliability
If calibration boards are used for quality checks, then calibration quality can be evaluated, but the procedure requires numerous poses and high image resolution leading to several hours of computing time
Solution Approach 1:
The system performs self-assessment of calibration quality by using the camera array itself to evaluate its own calibration state. Through feature matching and 3D reconstruction, the system automatically determines calibration quality without requiring external calibration boards or manual intervention, enabling rapid online quality checks that do not disrupt production workflows
Solution Approach 2:
The patent changes the assessment parameters from traditional calibration board measurements to feature matching metrics and 3D reconstruction error metrics. By using reprojection error and feature correspondence quality as calibration quality indicators, the system achieves reliable assessment with significantly reduced processing time compared to conventional high-resolution calibration board methods
3Measurement precision
If conventional calibration procedures are performed, then comprehensive calibration assessment is achieved, but the process is labor-intensive requiring manual positioning and processing
Solution Approach 1:
The patent replaces the mechanical calibration board positioning system with a computational feature matching system. Instead of manually positioning calibration boards at numerous locations, the system uses automated feature detection and matching algorithms that work with natural or projected features in the scene, eliminating manual mechanical operations while maintaining comprehensive calibration assessment through multi-camera feature correspondence
Data Source
AI summary
Systems and methods for assessing the calibration of an array of cameras. The method including inputting into a processor captured images from at least two cameras of the array of cameras, the captured images having features from an image. The method further including extracting one or more extracted features from the captured images, matching one or more extracted features between pairs of the at least two cameras to create a set of matched features, selecting matching points from the set of matched features, generating a three-dimensional reconstruction of objects in a field of view of the at least two cameras, and outputting the three-dimensional reconstruction wherein the three-dimensional reconstruction comprises indicators of calibration errors.