Gyroscope drift on mobile phones

Question

Lots of posts talk about the gyro drift problem. Some guys say that the gyro reading has drift, however others say the integration has drift.

1. The raw gyro reading has drift[link].
2. The integration has drift[link](Answer1).

So, I conduct one experiment. The next two figures are what I got. The following figure shows that gyro reading doesn't drift at all, but has the offset. Because of the offset, the integration is horrible. So it seems that the integration is the drift, is it?

The next figure shows that when the offset is reduced the integration doesn't drift at all.

In addition, I conducted another experiment. First, I put the mobile phone stationary on the desk for about 10s. Then rotated it to the left then restore to back. Then right and back. The following figure tells the angle quite well. What I used is only reducing the offset then take the integration.

So, my big problem here is that maybe the offset is the essence of the gyro drift(integration drift)? Can complimentary filter or kalman filter be applied to remove the gyro drift in this condition?

Any help is appreciated.

Answer 1

If the gyro reading has "drift", it is called bias and not drift.

The drift is due to the integration and it occurs even if the bias is exactly zero. The drift is because you are accumulating the white noise of the reading by integration.

For drift cancellation, I highly recommend the Direction Cosine Matrix IMU: Theory manuscript, I have implemented sensor fusion for Shimmer 2 devices based on it.

(Edit: The document is from the MatrixPilot project, which has since moved to Github, and can be found in the Downloads section of the wiki there.)

If you insist on the Kalman filter then see https://stackoverflow.com/q/5478881/341970.

By why are you implementing your own sensor fusion algorithm?

Both Android (SensorManager under Sensor.TYPE_ROTATION_VECTOR) and iPhone (Core Motion) offers its own.

Answer 2

The dear Ali wrote something that is really questionable and imprecise (wrong).

The drift is the integration of the bias. It is the visible "effect" of bias when you integrate. The noise - any kind of stationary noise - that has mean zero, consequently has integral zero (I am not talking of the integral of PSD, but of the additive noise of the signal integrated in time).

The bias changes in time, as a function of voltage and exercise temperature. E.g. if voltage changes (and it changes), bias changes. The bias it is not fixed nor "predictable". That is why you can not eliminate bias using the proposed subtraction of the estimated bias by the signal. Also any estimate has an error. This error cumulates in time. If the error is lower, the effects of cumulation (the drifting) become visible in a longer interval, but it still exists.

Theory says that a total elimination of bias it is not possible, at the present days. At the state of the art, no one has still found a way to eliminate the bias - based only gyroscopes and accelerometers magnetometers - that could filter all the bias out.

Android and iPhone have limited implementations of bias elimination algorithms. They are not totally free by bias effects (e.g. in small intervals). For some applications this can cause severe problems and unpredictable results.