Vehicle Camera Calibration Using Distortion-Corrected Pattern Iteration
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Solution Overview
Problem
Existing methods for calibrating the position and orientation of a camera mounted on a vehicle, such as a truck, are prone to inaccuracies due to image distortions caused by wide-angle optics and unreliable identification of characteristic points, especially in unfavorable lighting conditions, leading to false results and increased time and cost.
Innovation Solution
A method that compensates for image distortions by determining parameters of the calibration pattern, transforming the image based on these parameters, and iteratively refining the identification of characteristic points to improve precision and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If wide-angle optics are used to monitor a larger area, then the field of view is increased, but image distortions occur that hamper the identification of characteristic points
Solution Approach 1:
The patent applies preliminary action by transforming the distorted image before identifying characteristic points. The image transformation is performed in advance to compensate for wide-angle distortions, making the characteristic points more identifiable. This preprocessing step corrects the geometric distortions caused by wide-angle optics, allowing accurate identification of calibration pattern features even when using wide-field cameras.
2Ease of operation
If template matching is used to identify characteristic points, then the identification process is simplified, but false positives occur especially in unfavorable lighting conditions
Solution Approach 1:
The patent implements feedback by iteratively refining the identification of characteristic points. After initial template matching, the results are evaluated and used to adjust subsequent identification attempts. The system uses the identified points to transform the image and re-identify points, creating a feedback loop that improves accuracy. This iterative process allows the system to correct initial false positives by comparing results across multiple iterations with progressively improved image transformations.
3Adaptability or versatility
If calibration is performed during vehicle movement to use image differences, then calibration information can be derived from dynamic conditions, but the process becomes time-consuming and costly
Solution Approach 1:
The patent applies self-service by enabling calibration to be performed by the camera system itself using a simple calibration pattern and image processing algorithms. The system automatically identifies characteristic points, transforms images, and calculates calibration parameters without requiring external equipment or complex procedures. This self-calibration capability allows the system to be performed quickly on a stationary vehicle, eliminating the need for time-consuming dynamic calibration methods while maintaining adaptability to different mounting conditions.
Data Source
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AI summary
A method for calibrating the position and/or orientation of a camera, in particular a camera mounted to a vehicle such as a truck, relative to a calibration pattern comprises the steps of: A. acquiring an image of the calibration pattern by means of the camera; B. determining at least one parameter of the image and/or of the calibration pattern or a sub-pattern of the calibration pattern as it appears in the image; C. transforming the image based on the at least one parameter; D. identifying characteristic points or possible characteristic points of the calibration pattern within the transformed image of the calibration pattern; E. deriving the position and/or orientation of the camera relative to the calibration pattern from the identified characteristic points or possible characteristic points; F. in dependence of a confidence value of the derived position and/or orientation of the camera and/or in dependence of the number of iterations of steps B to F so far, repeating steps B to F, wherein in step B the derived position and/or orientation of the camera are taken into account for determining the at least one parameter; and G. outputting the position and/or orientation of the camera derived in the last iteration of step E.