AR Sensor Miscalibration Detection for On-Demand Recalibration
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Solution Overview
Problem
Augmented reality (AR) and mixed reality (MR) devices suffer from miscalibration issues due to sensor deformations, leading to inaccuracies in virtual content positioning, which current recalibration methods consume significant processing resources and introduce latency.
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 for deformations and adjusting sensor parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recalibration is performed frequently to maintain sensor alignment accuracy, then measurement precision is improved, but use of energy and processing resources increases significantly
Solution Approach 1:
The system performs preliminary detection of miscalibration conditions using sensor data analysis and machine learning models before initiating full recalibration. This allows the system to prepare and trigger recalibration only when actual miscalibration is detected, avoiding unnecessary processing and energy consumption while maintaining measurement precision.
2Measurement precision
If recalibration is performed frequently to maintain sensor alignment accuracy, then measurement precision is improved, but productivity decreases due to processing overhead
Solution Approach 1:
The system uses preliminary sensor data monitoring and machine learning-based detection to identify when miscalibration has occurred before initiating full recalibration. This selective approach maintains high measurement precision while significantly improving processing efficiency by avoiding unnecessary recalibration operations.
Solution Approach 2:
The system autonomously monitors its own sensor calibration status using onboard sensors and machine learning models, automatically detecting miscalibration conditions and triggering recalibration only when needed. This self-service capability eliminates the need for continuous external monitoring and manual recalibration scheduling, optimizing both precision and productivity.
3Measurement precision
If recalibration is performed frequently to maintain sensor alignment accuracy, then measurement precision is improved, but loss of time increases due to recalibration latency
Solution Approach 1:
The system continuously monitors sensor data and uses machine learning models to detect miscalibration conditions in advance of actual performance degradation. By triggering recalibration only when miscalibration is detected, the system maintains measurement precision while minimizing time loss by avoiding unnecessary recalibration operations and their associated latency.
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.