AR Sensor Miscalibration Detection for Selective Recalibration
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Solution Overview
Problem
Augmented reality (AR) and mixed reality (MR) devices suffer from miscalibration issues due to deformations of sensors, leading to inaccuracies in the alignment of virtual content with the real-world environment, which can degrade user experience and consume significant processing resources.
Innovation Solution
A computer vision system uses a machine learning model to dynamically detect miscalibrations and trigger recalibration only when necessary, reducing unnecessary processing overhead by assessing sensor data with lower computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic recalibration is performed frequently to maintain sensor alignment accuracy, then measurement precision is improved, but processing resources are consumed excessively
Solution Approach 1:
The system continuously monitors sensor data and uses a machine learning model to detect miscalibration conditions in real-time. When miscalibration is detected, recalibration is triggered. This feedback mechanism ensures recalibration occurs only when necessary, maintaining accuracy while avoiding unnecessary processing resource consumption.
Solution Approach 2:
The system transitions from static periodic recalibration to dynamic condition-based recalibration. The recalibration frequency and timing adapt based on actual sensor conditions detected by the machine learning model, allowing the system to optimize between accuracy maintenance and resource conservation.
2Reliability
If recalibration is performed continuously to maintain high-quality output, then reliability is improved, but productivity is reduced due to processing overhead
Solution Approach 1:
The machine learning model provides continuous feedback on sensor calibration status, enabling the system to maintain high output quality by triggering recalibration only when miscalibration is actually present, rather than performing redundant recalibration operations that would reduce productivity.
Solution Approach 2:
The system autonomously monitors its own calibration status and self-corrects when needed through automated recalibration triggered by the machine learning detection, eliminating the need for manual intervention while maintaining reliability without excessive processing overhead.
3Measurement precision
If a machine learning model is used to detect miscalibration conditions, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary layer between raw sensor data and recalibration control decisions. This intermediary processes sensor data to detect miscalibration conditions with high precision, enabling accurate detection while keeping the overall system architecture modular and manageable despite the added complexity.
4Productivity
If selective recalibration is implemented to reduce processing overhead, then productivity is improved, but measurement precision may deteriorate due to missed recalibration events
Solution Approach 1:
The machine learning model provides continuous feedback on sensor calibration status, enabling the system to maintain high output quality by triggering recalibration only when miscalibration is actually present, rather than performing redundant recalibration operations that would reduce productivity.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based recalibration triggers with a machine learning-based detection system. This substitution enables more precise detection of actual miscalibration conditions, allowing selective recalibration that maintains accuracy while improving processing efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing miscalibration detection. One of the methods includes receiving sensor data from each of multiple sensors of a device in a system configured to provide augmented reality or mixed reality output to a user. Feature values are determined based on the sensor data for a predetermined set of features. The determined feature values are processed using a miscalibration detection model that has been trained, based on examples of captured sensor data from one or more devices, to predict whether a miscalibration condition of one or more of the multiple sensors has occurred. Based on the output of the miscalibration detection model, the system determines whether to initiate recalibration of extrinsic parameters for at least one of the multiple sensors or to bypass recalibration.