Camera Calibration via High-Precision Map Projection
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Solution Overview
Problem
Traditional camera calibration methods for vehicle-to-everything (V2X) roadside sensing systems, such as those using checkerboards, are labor-intensive and time-consuming, requiring significant manual effort and resources.
Innovation Solution
A camera calibration method that utilizes a high-precision map to establish a least square optimization algorithm for camera calibration, eliminating the need for calibration plates and enabling self-calibration of internal parameters without additional image collection, thereby reducing labor and time costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods using checkerboards are employed, then calibration accuracy can be achieved, but labor cost and time cost increase significantly
Solution Approach 1:
The system performs self-calibration by automatically comparing captured images with pre-stored reference images of the same scene. The calibration process does not require manual placement of checkerboards or intervention, as the electronic device autonomously identifies feature points in both the captured and reference images, computes transformation parameters, and updates camera calibration data independently.
Solution Approach 2:
The method uses a pre-captured reference image as a template to compare against newly captured images. By copying the scene geometry and feature point relationships from the reference image, the system establishes correspondence without requiring physical calibration artifacts, thereby eliminating manual setup time while maintaining calibration accuracy.
2Measurement precision
If traditional calibration methods using checkerboards are employed, then calibration accuracy can be achieved, but labor cost increases
Solution Approach 1:
The system performs self-calibration by automatically comparing captured images with pre-stored reference images of the same scene. The calibration process does not require manual placement of checkerboards or intervention, as the electronic device autonomously identifies feature points in both the captured and reference images, computes transformation parameters, and updates camera calibration data independently.
Solution Approach 2:
The method extracts and removes the dependency on manual calibration artifacts (checkerboards) from the calibration process. By extracting only the essential calibration function—comparing image geometries—and implementing it through automated image matching algorithms, the system eliminates the need for operators to physically handle and position calibration plates.
3Reliability
If manual calibration plate placement is required, then calibration can be performed, but the process becomes complex and resource-intensive
Solution Approach 1:
The system performs self-calibration by automatically comparing captured images with pre-stored reference images of the same scene. The calibration process does not require manual placement of checkerboards or intervention, as the electronic device autonomously identifies feature points in both the captured and reference images, computes transformation parameters, and updates camera calibration data independently.
Solution Approach 2:
The calibration method is universally applicable to various camera types and mounting configurations without requiring different calibration artifacts. The same image-matching-based approach works for both single-camera and multi-camera systems, whether cameras are mounted on vehicles, poles, or other structures, thereby simplifying the overall calibration process while maintaining reliability.
Data Source
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AI summary
A camera calibration method and apparatus, an electronic device, a storage medium, a computer program product, and a road side device are provided, and relate to the technical field of intelligent transportation and vehicle and infrastructure cooperation. The method includes: determining an initial value of an internal parameter of a camera, an initial value of an external parameter of the camera, and an initial value of a distortion coefficient of the camera; determining a conversion relationship of a point in an image captured by the camera from a world coordinate system to a pixel coordinate system, based on the initial value of the internal parameter, the initial value of the external parameter, the initial value of the distortion coefficient and a mathematical model of the camera; projecting a plurality of feature points in a high-precision map of a first area onto the pixel coordinate system of a first image, of the first area, captured by the camera to obtain a plurality of projection points; and obtaining the internal parameter and the distortion coefficient of the camera through a least square method, based on coordinates of the plurality of feature points in the pixel coordinate system, the conversion relationship and world coordinates of the plurality of projection points.