Automatic Camera Calibration for Multi-Stage Lens Distortion Estimation
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Solution Overview
Problem
Current camera calibration methods in robotic systems often rely on manual operations and are prone to inaccuracies due to complex lens distortion, which can lead to reduced accuracy in estimating lens distortion parameters and stereo camera calibration.
Innovation Solution
A computing system that performs camera calibration by receiving calibration images, determining image coordinates, and estimating lens distortion parameters through multiple stages, using a sequence of stages to refine estimates and account for complex distortion effects, and determining a transformation function for stereo calibration to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual camera calibration is performed, then the process can be completed with simple equipment, but the calibration accuracy is reduced due to complex lens distortion
Solution Approach 1:
The calibration process is divided into multiple sequential stages: first determining initial camera parameters without distortion, then iteratively refining lens distortion parameters, and finally determining stereo calibration parameters. This segmentation allows each stage to focus on specific parameters, improving overall calibration accuracy while managing complexity through structured progression.
Solution Approach 2:
The method performs preliminary determination of camera parameters before addressing lens distortion. By first establishing baseline calibration data without distortion effects, then progressively adding distortion correction in subsequent stages, the system builds a foundation that improves final accuracy without overwhelming complexity.
2Measurement precision
If lens distortion parameters are estimated in a single stage, then the calibration process is simpler, but the estimation accuracy is reduced
Solution Approach 1:
Lens distortion parameter estimation is divided into multiple iterative stages rather than a single step. Each stage refines the distortion parameters based on results from the previous stage, progressively improving accuracy. This multi-stage approach balances the trade-off between calibration efficiency and parameter estimation accuracy.
Solution Approach 2:
The calibration method uses feedback from each iteration stage to improve subsequent estimates. The determined parameters from one stage serve as inputs for the next stage, creating a feedback loop that progressively refines lens distortion parameter accuracy while maintaining reasonable calibration efficiency through automated iteration.
3Loss of time
If automated calibration is implemented, then calibration time is reduced, but the system complexity increases
Solution Approach 1:
The calibration system performs self-calibration through automated image capture and parameter determination. The robot controller automatically captures calibration images, processes them through the multi-stage calibration algorithm, and determines calibration parameters without requiring manual intervention for each measurement step, significantly reducing calibration time while managing complexity through integrated automation.
Solution Approach 2:
The method replaces manual mechanical calibration operations with automated image-based calibration. Instead of physical measurement and adjustment, the system uses camera images processed through computational algorithms to determine calibration parameters, reducing time loss while shifting complexity from mechanical operations to computational processing.
Data Source
AI summary
A system and method for performing automatic camera calibration is provided. The system receives a calibration image, and determines a plurality of image coordinates for representing respective locations at which a plurality of pattern elements of a calibration pattern appear in a calibration image. The system determines, based on the plurality of image coordinates and defined pattern element coordinates, an estimate for a first lens distortion parameter of a set of lens distortion parameters, wherein the estimate for the first lens distortion parameter is determined while estimating a second lens distortion parameter of the set of lens distortion parameters to be zero, or is determined without estimating the second lens distortion parameter. The system determines, after the estimate of the first lens distortion parameter is determined, an estimate for the second lens distortion parameter based on the estimate for the first lens distortion parameter.