AVM Camera Calibration Using Virtual Ground Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing around-view monitoring (AVM) systems require lengthy manual calibration due to unknown relative positions and poses of multiple cameras, leading to misalignment and inefficiency in image stitching, placing a significant burden on operators.
Innovation Solution
A calibration method that extracts local patterns from images captured by each camera, generates overhead-view images, and matches these patterns to determine camera parameters and transformation information, allowing for automated calibration of image capture devices to generate accurate AVM images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration with ground patterns is used, then camera parameters can be determined, but calibration time and operator burden increase significantly
Solution Approach 1:
The patent uses virtual ground patterns generated from overhead view images as a copy of the actual physical ground patterns. Instead of requiring operators to manually place and match physical checker board patterns, the system creates virtual patterns that represent the ground truth geometry, allowing automated matching and calibration parameter determination without manual intervention.
Solution Approach 2:
The patent replaces the mechanical manual calibration process with an automated computer vision-based system. The overhead view camera captures images, virtual ground patterns are generated through image processing, and automated feature matching algorithms determine camera parameters, eliminating the need for manual tape measurement and pattern placement.
2Measurement precision
If manual calibration with precise pattern placement is required, then accurate camera parameters can be obtained, but operation complexity and difficulty increase
Solution Approach 1:
The system performs self-calibration by automatically capturing overhead view images, generating virtual ground patterns, extracting feature points, and computing camera parameters without requiring operator intervention for pattern placement or matching. The calibration process serves itself through automated image processing and computation.
Solution Approach 2:
The patent introduces virtual ground patterns as an intermediary between the overhead view camera and the calibration parameter determination. These virtual patterns serve as a mediator that translates physical camera positions into computable calibration parameters through automated feature matching, eliminating the need for direct manual measurement and pattern placement.
3Adaptability or versatility
If multiple cameras are used for 360-degree coverage, then around-view monitoring capability is improved, but relative positioning and alignment between cameras become more complex
Solution Approach 1:
The patent applies a universal calibration approach that works for multiple cameras simultaneously. The overhead view camera and virtual ground pattern method provide a common reference frame that can be used to calibrate all image capture devices in the system, regardless of their specific positions or orientations, simplifying the multi-camera calibration process.
Solution Approach 2:
The patent introduces a new dimension for calibration by using overhead view imaging. Instead of calibrating cameras from their individual perspectives, the system captures images from an overhead dimension, generates virtual ground patterns in this new dimension, and uses them to determine relative positions and poses of all cameras, simplifying the complex multi-camera alignment problem.
Data Source
AI summary
Calibration methods for calibrating image capture devices of an around view monitoring (AVM) system mounted on vehicle are provided, the calibration method including: extracting local patterns from images captured by each image capture device, wherein each local pattern is respectively disposed at a position within the image capturing range of one of the image capture devices; acquiring an overhead-view (OHV) image from OHV point above vehicle, wherein the OHV image includes first patterns relative to the local patterns for the image capture devices; generating global patterns from the OHV image using the first patterns, each global pattern corresponding to one of the local patterns; matching the local patterns with the corresponding global patterns to determine camera parameters and transformation information corresponding thereto for each image capture device; and calibrating each image capture device using determined camera parameters and transformation information corresponding thereto so as to generate AVM image.