Vision System Calibration Using Environmental Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vision system calibration methods for surround view systems are limited by error propagation from inaccurate marker positioning, require expensive technologies, or depend on precise calibration of individual cameras, and often rely on predetermined markers, which can be hazardous due to blind spots and inaccurate space representation.
Innovation Solution
A calibration system that captures images from multiple cameras with overlapping fields of view, determines the position and orientation of each camera relative to objects in the environment without relying on predetermined marker positions, using software, hardware, or firmware to establish intrinsic and extrinsic parameters, allowing for accurate calibration of image sensors with non-linear distortion, such as fisheye lenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods using predetermined markers are used, then calibration can be performed, but error propagation occurs due to inaccurate marker positioning and the markers create blind spots in the vision system
Solution Approach 1:
The patent removes the calibration marker from the calibration process entirely. Instead of using physical markers that create blind spots and positioning errors, the system uses the natural environment (road surface features, building corners, etc.) as calibration targets. This extraction of the marker eliminates the harmful effects while maintaining calibration functionality.
Solution Approach 2:
The patent introduces an intermediary processing layer that automatically detects and identifies calibration targets in the captured images. The system uses image processing algorithms to find feature points on the road surface or surrounding environment, match them with known calibration data, and compute camera parameters without direct physical marker contact.
2Measurement precision
If expensive calibration technologies are used, then calibration precision can be improved, but system cost increases
Solution Approach 1:
The patent replaces expensive calibration equipment with free, readily available environmental features. Instead of requiring specialized calibration boards or markers that cost money and time to position, the system uses any natural or man-made features in the environment (road markings, building corners, tree patterns) as calibration targets, eliminating equipment costs entirely.
Solution Approach 2:
The system performs self-calibration using automatically detected environmental features. The image processing algorithm automatically identifies calibration targets, extracts their positions, and computes camera intrinsics and extrinsics without requiring manual intervention or expensive tools. The system serves its own calibration needs using freely available visual information from the environment.
3Measurement precision
If individual camera calibration is performed, then each camera can be calibrated, but the process becomes complex and time-consuming
Solution Approach 1:
The patent merges the calibration process into a single unified operation that simultaneously calibrates multiple cameras. Instead of calibrating each camera separately through individual procedures, the system captures images with multiple cameras and performs a single joint calibration computation that determines all camera intrinsics and extrinsics at once, reducing overall complexity and time requirements.
Solution Approach 2:
The calibration system is designed as a universal multi-functional process that handles multiple cameras and multiple calibration targets simultaneously. The same image processing algorithm works for all cameras and all detected calibration features, providing a single comprehensive calibration solution rather than multiple separate calibration procedures.
Data Source
AI summary
A system for calibrating a vision system of an object. For example, a system for calibrating a surround view system of a vehicle. The system may be configured to capture, via a first and second image sensor, a first and second image of a plurality of objects. Upon capturing such information, the system may be configured to determine a position and orientation of the first and the second image sensor relative to the plurality of objects based on the first and the second captured image. Further, the system may be configured to determine a relative position and orientation between the first and the second image sensor based on a result of the determining a position and orientation of the first and the second image sensor. Also, the system may be configured to determine intrinsic and extrinsic parameters of the first and the second image sensor.