MR Motion Model Adaptation for Moving Platform Holograms
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Solution Overview
Problem
Conventional MR systems struggle to accurately display world-locked holograms in moving environments, as existing motion models are not effectively fine-tuned for different types of moving platforms, leading to display artifacts such as hologram shifting or jumping.
Innovation Solution
The use of a trained predictive machine learning algorithm that categorizes moving platforms based on convoluted motion data, allowing for the fine-tuning of motion model parameters to accurately compensate for the motion of specific platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a generic motion model is used for all moving platforms, then the system complexity is reduced and ease of operation is improved, but the manufacturing precision and reliability of hologram display deteriorate due to inability to compensate for platform-specific motion characteristics
Solution Approach 1:
The patent changes the parameters of the motion model based on the detected platform type. Different platforms (vehicle, elevator, aircraft) have different motion characteristics, and the system adjusts motion model parameters accordingly to maintain hologram display precision while operating on various platforms
Solution Approach 2:
The system dynamically adapts the motion model parameters in real-time based on platform detection. The motion model transitions from a static generic configuration to a dynamic platform-specific configuration, allowing the hologram display to compensate for different motion patterns of various platforms
2Reliability
If platform-specific motion model parameters are used, then the reliability and precision of hologram display is improved, but the device complexity increases due to need for multiple parameter sets and platform classification
Solution Approach 1:
The system automatically detects the platform type and selects appropriate motion model parameters without requiring manual user input or configuration. The automated platform detection and parameter selection process reduces the perceived complexity for users while maintaining high reliability
Solution Approach 2:
The motion model is designed to be universal and adaptable to multiple platform types. Rather than requiring separate dedicated models for each platform, a single motion model structure is used with configurable parameters that can be adjusted to fit different platform characteristics, reducing overall system complexity
3Ease of operation
If the motion model parameters are not fine-tuned for different platforms, then the ease of manufacture and operation is maintained, but display artifacts such as hologram shifting or jumping occur
Solution Approach 1:
The system changes motion model parameters based on detected platform characteristics to prevent display artifacts. By adapting parameters to match platform-specific motion patterns, the system eliminates hologram shifting and jumping that would otherwise occur with generic parameters
Data Source
AI summary
Techniques for intelligently identifying what type of moving platform an MR system is operating on are disclosed. A display artifact that is associated with content displayed by the MR system is detected. A determination is made that a current configuration of a motion model used to display the content is causing the display artifact. Time-limited series of convoluted motion data is analyzed. The time-limited series of convoluted motion data is fed as input to a predictive ML algorithm. The predictive ML algorithm determines a particular category for the moving platform based on the time-limited series of convoluted motion data. Based on the determined category, either a reconfigured version of the motion model is used or a new motion model is used to display a hologram.