Sensor Calibration via Motion Observation Comparison
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Solution Overview
Problem
Computing devices with multiple sensors face challenges in maintaining accurate data collection due to sensor calibration requirements, as sensors may need adjustments over time or after physical disruptions, leading to inaccurate information unless regularly calibrated.
Innovation Solution
A method and system for calibrating sensors in computing devices, which involves receiving information on calibrated sensors, comparing independent motion observations from different sensors, and adjusting parameters of non-calibrated sensors to align their outputs within a threshold variance of calibrated sensor estimations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are regularly calibrated to maintain accurate data collection, then measurement precision is improved, but device complexity and maintenance requirements increase
Solution Approach 1:
The system performs self-calibration by automatically comparing sensor outputs without requiring external calibration equipment or manual intervention. The computing device uses its own sensors to cross-validate and adjust each other's readings, making the calibration process autonomous and eliminating the need for complex external calibration systems.
Solution Approach 2:
The system continuously monitors sensor outputs and automatically adjusts sensor parameters based on discrepancies detected during operation. This closed-loop feedback mechanism ensures sensors remain calibrated by comparing readings and making real-time adjustments, eliminating the need for periodic manual calibration while maintaining measurement precision.
2Adaptability or versatility
If multiple sensors are used to capture various information for the computing device, then adaptability and data comprehensiveness are improved, but ensuring all sensors provide accurate synchronized data becomes more difficult
Solution Approach 1:
The system merges data from multiple sensors by comparing their outputs and determining whether they are within a threshold variance of each other. This combination approach allows the device to leverage information from diverse sensors while maintaining data reliability through cross-validation, ensuring all sensors provide accurate synchronized data.
3Measurement precision
If sensor parameters are automatically adjusted based on calibration algorithms, then measurement precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs calibration adjustments only when discrepancies between sensor readings exceed a predefined threshold, rather than continuously adjusting all sensors. This partial action approach reduces unnecessary processing while maintaining measurement precision by focusing computational resources only on sensors that require calibration.
Data Source
AI summary
Methods and systems for calibrating sensors on a computing device are described herein. In an example implementation, a computing device may perform a method to calibrate one or more sensors, which may include receiving an indication that a sensor has been calibrated. The computing device may further receive independent observations of a motion of a device from the calibrated sensor and a potentially non-calibrated sensor. The device may determine as independent estimation of motion based on the movement of the device corresponding to the outputs of the respective sensors. Based on whether or not the estimation of motion as provided by the potentially non-calibrated sensor is within a threshold variance of the estimation of motion as provided by the calibrated sensor, the computing device may provide instructions to adjust parameters of the non-calibrated sensor.