Hybrid Eye Tracking Drift Correction
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Solution Overview
Problem
Eye tracking systems using Optical Coherence Tomography (OCT) in artificial reality contexts suffer from positional drift due to accumulated errors over time, as they determine eye positions based on previous determinations without independent validation.
Innovation Solution
A hybrid eye tracking system combining an OCT eye tracking unit with a reference tracking system, where the reference system periodically updates the OCT eye tracking positions to correct for drift, using a camera system to capture images and determine absolute eye positions independently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If OCT eye tracking determines position based on previous determinations, then tracking speed is improved, but measurement precision deteriorates due to accumulated errors causing drift
Solution Approach 1:
The system performs periodic drift correction by intermittently capturing OCT datasets and comparing them against the baseline model. Instead of continuous correction, the system samples at specific intervals to detect and correct drift, maintaining high tracking speed while periodically refreshing accuracy. The controller is configured to capture OCT datasets at a first rate and update the baseline model based on these periodic measurements.
Solution Approach 2:
The system establishes a feedback loop where the OCT eye tracking unit continuously monitors eye position and compares it against the baseline model. When drift is detected through periodic OCT dataset analysis, the system generates corrective updates to the baseline model, which then feeds back into the tracking algorithm to realign future measurements with actual eye position.
2Measurement precision
If reference tracking system updates OCT positions periodically, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges two eye tracking approaches into a unified hybrid system: OCT-based high-speed relative position tracking and reference-based absolute position determination. The OCT unit provides continuous relative motion data while the reference tracking system periodically anchors this data to absolute coordinates, creating a cohesive tracking solution that leverages the strengths of both methods.
Solution Approach 2:
The baseline model serves as an intermediary between the OCT eye tracking unit and the reference tracking system. It stores the relationship between OCT-derived relative positions and reference-derived absolute positions, enabling the system to translate and integrate data from both sources without requiring direct complex interaction between the tracking units themselves.
3Productivity
If OCT eye tracking operates at high rate, then productivity is improved, but loss of information increases due to error accumulation over time
Solution Approach 1:
The system performs preliminary drift detection and correction by periodically capturing OCT datasets and comparing them against the baseline model before error accumulation significantly degrades tracking accuracy. This proactive approach allows the system to maintain high-speed operation while preemptively correcting drift through baseline model updates.
Solution Approach 2:
The system dynamically adjusts operational parameters including the rate of OCT dataset capture and baseline model update frequency. By optimizing these parameters, the system maintains high productivity through fast eye position determination while preventing information loss through timely drift correction, balancing speed and accuracy based on performance requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This hybrid approach significantly reduces positional errors over time, maintaining accuracy and preventing drift, allowing for precise eye position tracking in real-time applications.
Implementation Method 1
an illumination source configured to project low coherence interference light onto a portion of the user's eye
Implementation Method 2
a detector configured to capture light reflected from the illuminated portion of the user's eye
Implementation Method 3
the reference tracking system comprises a camera imaging system configured to capture one or more images of the user's eye
Implementation Method 4
the reference tracking system determines the second position based upon the one or more captured images based upon one or more glints in the captured images
Data Source
AI summary
A system tracks eye position using a first Optical Coherence Tomography (OCT) eye tracking unit to determine a first position of an eye of a user. The OCT eye tracking unit projects low coherence interference light onto a portion of the user's eye, and uses captured light reflected from the illuminated portion of the user's eye to generate a dataset describing the portion of the user's eye. The first position of the user's eye is determined based upon the generated dataset and a previously generated dataset. In order to minimize error and reduce the effects of draft, a reference tracking system is configured to determine a second position of the user's eye, which is used to update the determined first position of the user's eye.