In-Drive Camera Calibration from Road Pattern Coordinate Mapping
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Solution Overview
Problem
Camera calibration in vehicles is challenged by changes in tire air pressure, vehicle pose, and passenger load, leading to discrepancies between actual and captured environmental information.
Innovation Solution
A method and apparatus that detects standardized road patterns, transforms image coordinates into world coordinates, and calibrates the camera by comparing predicted and reference sizes, adjusting parameters like pitch, roll, and height to align with the actual driving environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera calibration is performed in initial state, then coordinate system transformation information can be obtained, but calibration accuracy deteriorates when deformation occurs due to tire air pressure change, vehicle pose change, or passenger load change
Solution Approach 1:
The patent implements dynamic calibration by continuously detecting road patterns during vehicle operation and updating camera parameters in real-time. Instead of relying on static initial calibration, the system adapts to changing vehicle conditions (tire pressure, load, pose) by performing multiple calibration operations as the vehicle moves, thereby maintaining calibration accuracy despite environmental deformations.
Solution Approach 2:
The system uses feedback from detected road pattern positions and sizes to continuously adjust and refine camera calibration parameters. By comparing detected pattern characteristics with expected values and using the discrepancies to update calibration data, the system maintains accurate coordinate transformations even when vehicle conditions change between initial calibration and operation.
2Measurement precision
If camera parameters are adjusted to adapt to changing conditions, then calibration accuracy improves, but system complexity increases due to multiple calibration operations
Solution Approach 1:
The calibration system performs self-calibration by automatically detecting road patterns and computing calibration parameters without requiring external calibration tools or manual intervention. The vehicle's normal driving over marked roads provides the calibration data, eliminating the need for separate calibration procedures while maintaining accuracy.
Solution Approach 2:
The calibration system serves multiple functions: it calibrates the camera during normal driving operations, validates detection algorithms using known road patterns, and adapts to various vehicle conditions simultaneously. By using standard road markings as calibration targets, the system eliminates the need for specialized calibration equipment and integrates calibration into regular vehicle operation.
Data Source
AI summary
A processor-implemented method with calibration includes: detecting a preset pattern comprised in a surface of a road from a driving image of a vehicle; transforming image coordinates in an image domain of the pattern into world coordinates in a world domain; determining whether to calibrate a camera capturing the driving image by comparing a size predicted based on the world coordinates of the pattern and a reference size of the pattern; in response to a determination to calibrate the camera, determining relative world coordinates of the pattern with respect to a motion of the camera, using images captured by the camera at different time points; transforming the relative world coordinates of the pattern into absolute world coordinates of the pattern; and calibrating the camera using a corresponding relationship between the absolute world coordinates of the pattern and the image coordinates of the pattern.