Linear Acceleration Calibration
I have a way of calibrating the raw accelerometer data to compensate for the offsets and cross axis sensitivity. I use the fact that when the IMU is completely stationary, it's acceleration vector length should be equal to g. So I leave the IMU stationary at 10+ different orientations and then solve the system of equations to give me my calibration matrices. My corrected acceleration will be of the form A_c = M * (A - where M is essentially slopes correcting the cross axis sensitivity and B will be the linear offsets and A is the raw accelerometer data. For example my B may be:
-0.0289
0.0184
0.0309
and M may be:
0.9944 -0.0092 -0.0070
-0.0092 0.9983 -0.0016
-0.0070 -0.0016 0.9878
I am happy with calibrating the accelerometer this way and I get satisfactory results as during an actual tests I'm doing when the IMU is stationary, the mean acceleration vector is equal to almost exactly 1g.
Now the problem arises when I start using sensor fusion and take linear accelerations. Linear accelerations I get are offset by as much as 0.015g. I've tried calibrating it the same way I would with using raw accelerometer data, but I don't get appropriate results because of how that linear acceleration is calculated (I think).
So I have the following questions: is there a way to calibrate linear accelerations to make sure they have a mean that is closer to zero when IMU is stationary. If that is not possible, I want to know if I can log both raw accelerometer and fusion data as using the MetaBase app, if I select accelerometer, fusion sensors disappear and vice versa. I have metamotion C,R and metawear CPro sensors if that helps.
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