In-field Visor Characterization for HMD Distortion Correction
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Solution Overview
Problem
Helmet-mounted displays (HMDs) face performance issues due to characterization error when a new visor is installed without recharacterization, leading to image distortion, and existing solutions either result in reduced yield or are time-consuming.
Innovation Solution
A system and method for in-field characterization of visors using image detectors and a controller to detect and correct characterization errors by comparing detected images to a test pattern and determining correction functions for each eye position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If every visor is screened to tight tolerance to make them appear identical to the Display system, then characterization error is reduced, but manufacturing yield is reduced
Solution Approach 1:
The patent changes the approach from pre-screening visors at manufacturing to in-field characterization by detecting and correcting distortion parameters. The system captures images through the visor, compares them to reference images, and determines distortion characteristics to apply correction functions, thereby achieving tight tolerance performance without rejecting visors during manufacturing.
2Measurement precision
If the new visor and helmet are shipped back to the factory to characterize the visor and helmet as a pair, then characterization error is reduced, but time consumption is increased
Solution Approach 1:
The system enables the visor-helmet pair to self-characterize in the field without requiring factory equipment or personnel. The characterization device captures images, processes them to determine distortion, and generates correction functions autonomously, allowing the system to perform its own calibration whenever a visor is installed or replaced.
Solution Approach 2:
The system performs characterization and correction function generation in advance, before actual use. By capturing test images and determining distortion characteristics proactively, the system ensures optimal performance is established ahead of time, rather than waiting for performance degradation to occur.
3Ease of operation
If a new visor is installed without recharacterization, then ease of replacement is improved, but characterization error increases
Solution Approach 1:
The system implements a feedback loop where images captured through the installed visor are compared to reference images, and the detected distortion is used to generate correction functions. This feedback mechanism ensures that whenever a visor is installed, its specific characteristics are measured and corrected, maintaining high precision regardless of how easily the visor was replaced.
Data Source
AI summary
A system for characterizing a visor mounted to a helmet, the helmet having at least one projector for directing light to reflect off the visor to image to an eye position, is described. The system includes a mounting structure configured to mount an image detector to the helmet, the image detector arranged to detect an image at the eye position, and a controller. The controller is configured to provide a test pattern, receive a detected image from the image detector disposed at an eye position based on the test pattern, compare the detected image to the test pattern to determine a characterization error corresponding to a distortion of the detected image, determine a correction function to correct the distortion, and correct for the distortion based on the correction function.