Advanced IMU Parameterization

Original report (archived issue) by Michael Carroll (Bitbucket: Michael Carroll, GitHub: mjcarroll).

While updating the noise models, I thought of a few additional parameters that may be useful to simulate real IMUs (and potentially other sensors).

Sensor Properties
- Dynamic Range - The maximum range that a sensor can measure before the readings are clipped. It’s possible to saturate sensors that have higher sensitivity over a smaller dynamic range.
- Sensitivity - This may already be captured by precision value, but we may need to rework how precision is mapped over a given dynamic range.
- Sensor Bandwidth - Typically sensors will have either mechanically or electrically-imposed bandwidth limits such that higher frequency components are filtered out. For instance the ADIS16448 has a front-end lowpass filter at approximately 330 Hz. Other sensors like a magnetometer are even lower (25Hz).
Other Error Sources
- Sensor timing - we could potentially model things like clock jitter or skew.
- Scale Factor - A multiplier between the physical input and the measurement, typically parts-per-million
- Nonlinearity - A non-linear function that maps the physical input to the measurement.
- Misalignment - Could probably already be captured by slight perturbations in the pose of the sensor.
- Non-orthogonality - If the sensor isn’t manufactured perfectly, then some physical inputs on the body axes will “leak” into other channels
- Acceleration effect on Bias - In MEMS gyroscopes, high accelerations can lead to additional biases in the measurements.
- Tempertature effect on Bias - In MEMS gyroscopes, varying temperatures can lead to additional biases in the measurements.

gazebosim / gz-sensors