AR Headset Calibration via Inverse Image Cancellation
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Solution Overview
Problem
Augmented Reality (AR) headsets face challenges in calibration due to manufacturing deviations, user-specific differences in head shape and eye location, and shifting during use, which affect the accuracy of the AR experience, especially for wide-field-of-view displays.
Innovation Solution
An AR calibration system using eye tracking and raytracing techniques to create a distortion mapping transform that compensates for manufacturing deviations and user-specific variations, allowing real-time adjustment and calibration during use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration methods are used, then the calibration process is simple, but the accuracy of AR imagery is insufficient due to manufacturing deviations and user-specific differences
Solution Approach 1:
The calibration process is divided into multiple stages: factory calibration to capture manufacturing deviations, and user-specific calibration to account for individual head shape and eye location differences. This segmentation allows each stage to focus on specific aspects of accuracy without overwhelming complexity in a single process.
Solution Approach 2:
Factory calibration is performed in advance during manufacturing to capture and store distortion mapping transforms that compensate for manufacturing deviations. This preliminary action prepares the system with baseline accuracy, allowing user-specific calibration to build upon this foundation rather than starting from scratch.
2Area of moving object
If wide-field-of-view displays are used, then the AR experience is more immersive, but optical distortion increases making calibration more difficult
Solution Approach 1:
The system uses distortion mapping transforms with multiple parameters (including radial, tangential, and prismatic distortion coefficients) to model and compensate for optical distortions. By changing and refining these parameters through calibration, the system maintains accurate imagery across wide fields of view despite optical imperfections.
Solution Approach 2:
The calibration process uses feedback from cameras capturing calibration images to iteratively refine distortion mapping transforms. The system compares captured calibration images with expected patterns and adjusts distortion parameters accordingly, enabling accurate calibration even for wide-field-of-view optics that are difficult to manufacture with precision.
3Ease of manufacture
If the AR headset is designed for average user dimensions, then manufacturing is simplified, but it cannot accommodate user-specific variations in head shape and eye location
Solution Approach 1:
The headset is pre-configured with factory calibration data captured during manufacturing, establishing a baseline distortion mapping transform that accounts for manufacturing variations. This preliminary action allows the device to function adequately for average users while providing a foundation for user-specific customization.
Solution Approach 2:
The system transitions from static, fixed calibration to dynamic, user-specific calibration. By performing calibration during initial setup and enabling ongoing updates during use, the system adapts to individual user characteristics in real-time, transforming a static manufacturing process into a dynamic user-tailored experience.
4Productivity
If calibration is performed only during manufacturing, then the process is efficient, but it cannot compensate for headset shifting during use
Solution Approach 1:
Factory calibration efficiently captures manufacturing deviations and stores distortion mapping transforms for immediate use. This preliminary calibration provides rapid setup and efficient production by performing all necessary calibration during manufacturing without requiring time-consuming post-purchase calibration procedures.
Solution Approach 2:
The calibration process continues beyond manufacturing into the user experience phase. The system maintains calibration accuracy through ongoing updates that compensate for headset shifting during use, ensuring continuous reliability throughout the product lifecycle rather than a one-time factory process.
Data Source
AI summary
An AR calibration system for correcting AR headset distortions. A calibration image is provided to a screen and viewable through a headset reflector, and an inverse of the calibration image is provided to a headset display, reflected off the reflector and observed by a camera of the system while it is simultaneously observing the calibration image on the screen. One or more cameras are located to represent a user's point of view and aligned to observe the inverse calibration image projected onto the reflector. A distortion mapping transform is created using an algorithm to search through projection positions of the inverse calibration image until the inverse image observed by the camera(s) cancels out an acceptable portion of the calibration image provided to the screen as observed through the reflector by the camera, and the transform is used by the headset, to compensate for distortions.