Inertial Sensor Calibration via Stationary Data Capture
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Solution Overview
Problem
Existing methods for calibrating inertial sensors in-field face accuracy issues due to vibration errors from vehicle movement and limited temperature range calibration, leading to suboptimal attitude solutions in precision agriculture and surveying applications.
Innovation Solution
A method and inertial measurement unit that determine when the working equipment is stationary, power up a sensor subsystem to capture data without vibration, and update the thermal bias error model by fitting curves to temperature data over extended periods, weighting recent data captures for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If inertial sensors are calibrated in-field during vehicle operation, then calibration can be performed without removing sensors from working equipment, but vibration errors from vehicle movement and engine vibration degrade measurement accuracy
Solution Approach 1:
The system performs preliminary actions by capturing sensor data during vehicle operation before the actual calibration computation. Multiple datasets are collected during different operational states (engine running, vehicle stationary, vehicle moving) and then processed together to compute corrected calibration parameters that compensate for vibration effects.
Solution Approach 2:
The system uses feedback by continuously monitoring vehicle state (through odometer, GPS, or other sensors) to determine when the vehicle is stationary versus moving. Based on this feedback, the system selectively processes different datasets through different computation paths - using one computation path when stationary and another when moving - thereby adapting the calibration process to current operational conditions and reducing vibration-induced errors.
2Loss of time
If calibration is performed over a limited temperature range due to time and cost constraints, then calibration process remains manageable, but the thermal bias error model becomes inaccurate for extended temperature variations
Solution Approach 1:
The system implements continuity of useful action by continuously capturing and storing sensor data across multiple temperature conditions over extended periods. Instead of performing a single limited-duration calibration, the system accumulates data continuously during vehicle operation, capturing temperature variations and corresponding sensor readings over time, thereby building a comprehensive thermal bias model without requiring separate calibration sessions.
Solution Approach 2:
The system performs self-service by automatically capturing, storing, and processing calibration data without requiring external intervention or manual calibration procedures. The vehicle's existing operational cycles (engine on/off, temperature variations during parking) are utilized to automatically generate calibration datasets, eliminating the need for dedicated calibration time and resources while expanding the effective temperature range covered.
3Reliability
If sensor data is captured during vehicle operation to maintain continuous calibration, then calibration remains up-to-date with current temperature conditions, but power consumption increases
Solution Approach 1:
The system applies periodic action by capturing sensor data at specific intervals during vehicle operation rather than continuously. The system monitors vehicle state (through odometer, GPS, or other sensors) to determine appropriate sampling moments - capturing data when the vehicle is stationary or during predictable operational phases - thereby maintaining calibration currency while minimizing power consumption during high-energy vehicle operation periods.
Data Source
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AI summary
A method of calibrating inertial sensors of working equipment, such as a vehicle or survey equipment, includes determining whether the working equipment is in operation or not. Data is captured from inertial sensors and associated temperature sensors while the working equipment is out of operation. The captured data is used to update a thermal bias error model for the inertial sensors.