IMU Camera Overlay Updates for Low Motion-to-Photon Latency
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Solution Overview
Problem
Mixed-reality systems face challenges in aligning images from multiple cameras, leading to visible latency and lagging effects due to movement between image generation and display, which disrupts the user experience and hinders accurate hologram placement and generation.
Innovation Solution
The system uses inertial measurement unit (IMU) data to update the position of overlaid image content, reflecting subsequent camera movements and predicting future positions to minimize latency by shifting bounding elements in the overlaid image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image content from multiple cameras is aligned and overlaid to generate passthrough images, then visualization accuracy and hologram placement are improved, but processing time increases causing visible latency and lagging effects
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing transformation matrices for the bounding element based on predicted camera positions. Instead of performing full image alignment processing after camera movement, the system prepares alignment data in advance using IMU predictions, allowing the bounding element to be rapidly repositioned without real-time processing delays.
Solution Approach 2:
The patent replaces the mechanical/image-processing system with a predictive mathematical model. Instead of physically re-aligning image content based on actual camera positions (which requires time-consuming image processing), the system uses IMU data to predict future positions and calculates corresponding transformation matrices mathematically, substituting heavy image processing with lighter computational geometry operations.
2Measurement precision
If image processing is performed to align system camera and external camera content, then spatial relationship visualization is improved, but the latency between image generation and display increases
Solution Approach 1:
The patent implements feedback by continuously monitoring IMU data from both cameras and using this information to dynamically adjust the bounding element position. The system creates a closed-loop control where predicted camera movements based on IMU feedback are compensated for in real-time by updating transformation matrices, ensuring the bounding element remains accurately positioned without waiting for full image processing cycles.
Solution Approach 2:
The system performs preliminary calculation of transformation matrices based on predicted camera positions before the actual image display occurs. By using IMU data to anticipate camera movement and pre-computing the necessary spatial transformations, the system eliminates the need for time-consuming post-capture alignment processing.
3Manufacturing precision
If the bounding element position is updated based on actual camera movement after image generation, then alignment accuracy is maintained, but the processing cannot keep up with rapid camera movements
Solution Approach 1:
The patent applies preliminary action by using IMU data to predict future camera positions and pre-calculating transformation matrices before the cameras actually move to those positions. This predictive approach allows the bounding element to be positioned accurately in advance, eliminating the lag that would occur if waiting for actual movement to be measured and processed.
Solution Approach 2:
The patent replaces the reactive image-processing-based alignment system with a predictive mathematical model. Instead of measuring actual camera movement and then calculating alignment (which is too slow), the system uses IMU sensors to predict movement and computes transformation matrices through mathematical operations that are computationally lighter and faster than full image processing pipelines.
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 approach minimizes latency effects, providing accurate and robust passthrough images with enhanced visualization, enabling rapid target acquisition and improved user experience in mixed-reality environments.
Implementation Method 1
obtain system IMU data from an IMU of the system; obtain external IMU data from an IMU of the external camera; Based on the system IMU data and the external IMU data, determine an amount of movement that the system camera and/or the external camera have moved
Data Source
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AI summary
Techniques for updating a position of overlaid image content using IMU data to reflect subsequent changes in camera positions to minimize latency effects are disclosed. A "system camera" refers to an integrated camera that is a part of an HMD. An "external camera" is a camera that is separated from the HMD. The system camera and the external camera generate images. These images are overlaid on one another and aligned to form an overlaid image. Content from the external camera image is surrounded by a bounding element in the overlaid image. IMU data associated with both the system camera and the external camera is obtained. Based on that IMU data, an amount of movement that the system camera and/or the external camera have moved since the images were originally generated is determined. Based on that movement, the bounding element is shifted to a new position in the overlaid image.