Vehicle Camera Calibration Using Overlapping Feature Points
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Solution Overview
Problem
Existing camera calibration methods for vehicles are ineffective when the vehicle is traveling, as they require static reference patterns and cannot accurately account for changes in camera installation due to physical forces, leading to inaccuracies in generating bird's-eye view images.
Innovation Solution
A method and device for calibrating multiple vehicle cameras while the vehicle is in motion, using a processor to detect feature points in overlapping regions of interest from multiple camera views, match these points, and adjust extrinsic parameters to minimize errors between bird's-eye coordinates, thereby correcting camera alignment and generating accurate bird's-eye view images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera calibration is performed using static reference patterns, then calibration accuracy is improved, but the method cannot be applied when the vehicle is traveling
Solution Approach 1:
The system uses road patterns and markings that are naturally present in the environment as reference features, eliminating the need for external calibration equipment. The cameras calibrate themselves by finding common feature points in overlapping fields of view during normal vehicle operation, turning the vehicle's normal operation into a calibration opportunity.
Solution Approach 2:
The calibration method serves multiple functions: it calibrates cameras during normal vehicle operation without requiring separate calibration procedures, works with existing road infrastructure, and maintains accuracy while adapting to dynamic travel conditions. The same camera system used for normal operation is also used for calibration.
2Measurement precision
If camera extrinsic parameters are adjusted to account for physical forces during travel, then calibration accuracy during motion is improved, but the complexity of the calibration process increases
Solution Approach 1:
The system calculates errors between bird's-eye coordinates of matching feature points from different cameras and uses this feedback to iteratively adjust extrinsic parameters. This closed-loop approach continuously refines calibration accuracy by comparing actual observations with expected geometric relationships and correcting deviations.
Solution Approach 2:
The calibration method is designed to work dynamically during vehicle travel rather than requiring static conditions. Extrinsic parameters are adjusted based on real-time feature point matching results, allowing the system to adapt to changing physical forces and motion conditions while maintaining calibration accuracy.
3Measurement precision
If feature points are matched between overlapping regions of multiple camera views, then calibration accuracy is improved, but the time required for calibration increases
Solution Approach 1:
The calibration process is divided into discrete steps: obtaining images from multiple cameras, identifying overlapping regions of interest, detecting feature points within these regions, matching corresponding feature points, calculating bird's-eye coordinates, and adjusting extrinsic parameters. This segmentation allows for systematic processing and optimization of each step.
Solution Approach 2:
The system focuses calibration efforts on overlapping regions of interest rather than processing entire images. By limiting feature point detection and matching to these specific partial regions where cameras overlap, the computational burden is reduced while maintaining calibration accuracy in the critical overlapping areas.
Data Source
AI summary
A camera calibration method includes obtaining a plurality of images of surroundings of a vehicle captured by a plurality of cameras, setting a region of interest (ROI) in each of the images, detecting one or more feature points of the set ROIs, matching a first feature point of a first ROI and a second feature point of a second ROI based on the detected feature points, calculating a first bird-view coordinate of the first feature point and a second bird-view coordinate of the second feature point, and calibrating the cameras by adjusting an extrinsic parameter of each of the cameras based on an error between the first bird-view coordinate and the second bird-view coordinate.