Inertial Sensor Calibration Techniques and Applications

Inertial sensors, comprising accelerometers and gyroscopes, underpin a vast array of technologies from autonomous vehicles and aerospace navigation to wearable devices and mobile electronics. Calibration is indispensable for correcting systematic errors such as bias, scale factor non-idealities and axis misalignments, as well as compensating for stochastic variations induced by temperature, vibration and ageing. Traditional laboratory procedures employ precision turntables or interferometric references to determine error parameters under controlled rotations and orientations. Recent advances have expanded into autonomous calibration routines that leverage environmental cues or motion patterns, algorithmic schemes based on Kalman filtering and optimisation, and emerging data-driven strategies including meta-learning for rapid adaptation to individual sensors. Thermal compensation methods now optimise the number and sequence of calibration poses to suppress drift over wide temperature ranges. System-level calibration has also been enhanced through optimal path planning, reducing procedure time while maintaining high accuracy. Together, these techniques have facilitated the deployment of low-cost micro-electro-mechanical systems (MEMS) in demanding applications, delivering robust performance without reliance on elaborate laboratory setups and enabling continuous self-calibration in dynamic real-world conditions.

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Inertial Sensor Calibration Techniques and Applications publication trend

The graph below shows the total number of articles in inertial sensor calibration techniques and applications across all publications each year (not limited to Nature Index journals).

Technical terms

Inertial Measurement Unit (IMU): A sensor assembly combining accelerometers and gyroscopes to measure linear acceleration and angular rate.

Micro-Electro-Mechanical Systems (MEMS): Miniaturised mechanical and electro-optic components fabricated using microfabrication techniques.

Bias instability: Time-dependent drift in a sensor’s zero-output point affecting long-term accuracy.

Scale factor error: Deviation from the ideal proportionality between the physical input and sensor output.

Meta-learning: A machine-learning paradigm in which models are trained to adapt rapidly to new tasks with minimal data.

Kalman filter: A recursive algorithm that fuses measurements and dynamic models to estimate unknown variables and their uncertainties.

Delaunay triangulation: A mathematical method for dividing a plane into triangles based on a set of points, used here for spatial parameter estimation.

References

1. MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends. Micromachines (2022).
2. Meta-learning for few-shot sensor self-calibration to increase stress robustness. Engineering Applications of Artificial Intelligence (2024).
3. Three‐Axes Mems Calibration Using Kalman Filter and Delaunay Triangulation Algorithm. Applied Computational Intelligence and Soft Computing (2023).
4. Optimal Path Planning Method for IMU System-Level Calibration Based on Improved Dijkstra’s Algorithm. IEEE Access (2023).