XR Display Strategy Adaptation for Localization Accuracy
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Solution Overview
Problem
Existing extended reality (XR) systems face inaccuracies in positioning virtual content due to varying localization accuracy of electronic devices within physical environments, leading to undesirable placements of virtual objects, such as floating in mid-air.
Innovation Solution
Implementing a method that selects a display strategy for virtual content based on the accuracy of localizing an electronic device within a physical environment, using multiple display strategies such as world-locked, position-oriented, and display-locked, and providing notifications to improve localization accuracy by addressing conditions like insufficient lighting or rapid movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer-vision localization techniques are used to determine device location, then virtual content can be positioned in the physical environment, but localization accuracy varies leading to inaccurate positioning of virtual objects
Solution Approach 1:
The system dynamically adjusts the display strategy based on the determined localization accuracy level. When localization accuracy is high, world-locked display is used to position virtual objects accurately in the physical environment. When localization accuracy is low, the system switches to display-locked or position-oriented strategies to prevent inaccurate positioning, thus adapting the positioning behavior to the current localization quality.
Solution Approach 2:
The system changes the display strategy parameter based on the localization accuracy level. By monitoring the localization accuracy and switching between different display strategies (world-locked, position-oriented, display-locked), the system optimizes the positioning accuracy of virtual objects according to the current localization quality without requiring hardware changes.
2Measurement precision
If multiple display strategies are implemented to handle varying localization accuracy, then positioning accuracy of virtual objects improves, but system complexity increases
Solution Approach 1:
The display strategy is segmented into multiple distinct strategies (world-locked, position-oriented, display-locked), each optimized for specific localization accuracy levels. This segmentation allows the system to handle different localization quality scenarios with dedicated strategies, improving overall positioning accuracy while keeping each individual strategy relatively simple and well-defined.
Solution Approach 2:
The system implements a feedback mechanism that continuously monitors localization accuracy and automatically selects the appropriate display strategy. This feedback loop eliminates the need for complex manual configuration or user intervention, as the system autonomously adjusts the display strategy based on real-time localization quality, thereby managing system complexity through automated decision-making.
3Measurement precision
If notifications are provided to users about localization conditions, then user ability to improve localization accuracy enhances, but additional processing and communication overhead increases
Solution Approach 1:
The system performs preliminary assessment of localization conditions and proactively provides notifications to users before significant positioning errors occur. By detecting conditions such as insufficient lighting or rapid movement early and alerting users, the system enables preventive actions to maintain localization accuracy, reducing the need for corrective processing later.
Data Source
AI summary
Various implementations disclosed herein include devices, systems, and methods that provide XR in which virtual objects are positioned based on the accuracy of localizing an electronic device in a physical environment. In some implementations, the technique assesses the accuracy of localization (e.g., centimeter-level accuracy, room-level accuracy, and building-level accuracy) and dynamically adjusts a display strategy. In some implementations, the technique determines a condition causing inaccuracy (e.g., a semantic condition such as “too fast”, “too far”, “too dark”), and provides a notification (e.g., “too fast-slow down”, “too far-move closer”, “too dark-turn on a light”) at the electronic device based on the condition causing the inaccuracy in the localization.