Hi, I am working with the LSM6DS33 and the LIS3MDL on the Pololu MinIMU-9 v5. I wanted to use the mentioned RTIMULib2 but it does not support the mentioned sensors. So I extended the Library and the result can be found here:
I am able to receive readings from the gyro, accel and mag. However, I am not sure if I set the axis of the individual sensors correctly (the registers itself and inside the cpp (line 821) at the end).
I run the Linux/RTIMULibDemoGL and calibrated the mag and accel. However, when I select compass only, the blue axis (inside the demo program) points always down. Using fused data, I also see some erroneous effects. If I tilt the imu around one axis it works at first but then turns around another axis inside the demo app too. Another issue that I observe is when I rotate the imu around the z-axis: Inside the program, I see the imu rotating slighty at first but then it stops or even rotates back in the opposite direction.
Can someone take a look at the code and tell me what I am doing wrong or do I have to calibrate the mag/compass using the advanced octave script provided by RTIMULib?
It sounds like you have the direction of one or more sensor axes defined incorrectly in RTImulib.
Have you triple-checked that you completely understand the directions of the axes within the chips themselves, and how those directions relate to the carrier PCB?
When you have done that, verify your understanding by collecting raw data from each sensor in turn, as you rotate the board in space. Make sure that the output data relate properly to the axis definitions and the mounting arrangement.
Finally, make sure you have defined a right handed axial system in RTIMUlib. If you simply change the sign on one axis, you end up with a left handed system.
The axes of the LSM6DS33 (gyro and accel) and the LIS3MDL (mag) point in the same directions on the PCB. I noticed that the blue cone inside the RTIMULibDemoGl program pointed downward in the fused state. Therefore I tried to switch the z-axis of the accel and the mag. This lead to the blue cone pointing upward.
The accel calibration seems to work fine. However, displying accel only, I also do not see controlled rotations inside the application, when I rotate about a single axis.
When I select compass only inside the program, the blue-axis always points down. Also when I try to calibrate the magnetomer I get only negative values for Z no matter how I rotate the IMU.
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Gyro Data: X 2.38848; Y 0.477697; Z 0.0305433
Gyro Data: X 2.26631; Y 0.673173; Z 0.0586431
Gyro Data: X 2.05862; Y 0.821003; Z 0.0171042
Gyro Data: X 1.57237; Y 0.91752; Z -0.0280998
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Gyro Data: X 0.173486; Y -0.0305433; Z 0
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Rotate y-axis:
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Gyro Data: X -0.0244346; Y 0.0219911; Z -0.449597
Gyro Data: X -0.0109956; Y 0.00977384; Z -0.518014
Gyro Data: X 0.0146608; Y -0.0842994; Z -0.621861
Gyro Data: X 0.0293215; Y -0.0500909; Z -0.703717
Gyro Data: X 0; Y -0.0916298; Z -0.755029
Gyro Data: X 0.0439823; Y -0.0171042; Z -0.828333
Gyro Data: X 0.0403171; Y -0.130725; Z -1.15942
Gyro Data: X 0.0623083; Y 0.00977384; Z -1.09467
Gyro Data: X 0.0158825; Y -0.0354302; Z -1.102
Gyro Data: X -0.0232129; Y 0.031765; Z -1.10811
Gyro Data: X -0.0439823; Y 0.206472; Z -0.722043
Gyro Data: X -0.0598648; Y 0.241903; Z -0.937067
Gyro Data: X -0.0329867; Y 0.163712; Z -1.17408
Gyro Data: X -0.0146608; Y 0.076969; Z -0.900415
Gyro Data: X -0.0207694; Y 0.131947; Z -0.850324
Gyro Data: X -0.0390954; Y 0.0696386; Z -0.907746
Gyro Data: X 0.295659; Y 0.0305433; Z -0.955393
Gyro Data: X -0.00610865; Y 0.0904081; Z -1.04947
Gyro Data: X -0.0158825; Y 0.0586431; Z -1.04947
Gyro Data: X -0.00733038; Y -0.00244346; Z -1.05558
Gyro Data: X -0.0305433; Y 0.00977384; Z -1.05069
Gyro Data: X -0.0244346; Y 0.0171042; Z -0.908967
Gyro Data: X -0.0329867; Y -0.00122173; Z -0.907746
Gyro Data: X -0.0513127; Y 0.0745256; Z -0.850324
Gyro Data: X -0.0293215; Y 0.0806342; Z -1.15576
Gyro Data: X -0.0171042; Y 0.0244346; Z -0.933402
Gyro Data: X -0.00610865; Y -0.0342085; Z -0.880868
Gyro Data: X -0.0378736; Y 0.0733038; Z -0.814894
Gyro Data: X -0.045204; Y 0.0561996; Z -0.829555
Gyro Data: X -0.0329867; Y 0.0781908; Z -0.841772
Gyro Data: X 0.0122173; Y -0.0378736; Z -0.662178
Gyro Data: X 0.0219911; Y -0.00610865; Z -0.761138
Gyro Data: X 0.00366519; Y -0.0158825; Z -0.576657
Gyro Data: X 0.018326; Y 0.230907; Z -0.619417
Gyro Data: X -0.0232129; Y -0.045204; Z -0.625526
Gyro Data: X -0.0122173; Y -0.0232129; Z -0.511905
Gyro Data: X -0.0293215; Y 0.0598648; Z -0.486249
Gyro Data: X -0.031765; Y 0.0342085; Z -0.483805
Gyro Data: X -0.0305433; Y -0.013439; Z -0.520457
Gyro Data: X -0.0268781; Y -0.045204; Z -0.583987
Gyro Data: X -0.0232129; Y 0.120951; Z -0.486249
Gyro Data: X -0.00244346; Y 0.105069; Z -0.594983
Gyro Data: X 0.00366519; Y 0.0855211; Z -0.588874
Gyro Data: X 0.311541; Y -0.0476475; Z -0.70616
Gyro Data: X -0.00488692; Y -0.245568; Z -0.619417
Gyro Data: X 0.0158825; Y -0.0586431; Z -0.689056
Gyro Data: X -0.0122173; Y 0.00855211; Z -0.692721
Gyro Data: X 0.0207694; Y -0.0940733; Z -0.752586
Gyro Data: X 0.00855211; Y -0.0195477; Z -0.946841
Gyro Data: X 0.00244346; Y -0.0610865; Z -1.01648
Gyro Data: X -0.00610865; Y 0.0268781; Z -1.07879
Gyro Data: X -0.0158825; Y -0.111177; Z -1.13865
Gyro Data: X -0.018326; Y -0.00733038; Z -1.16064
Gyro Data: X -0.0122173; Y -0.0109956; Z -1.05924
Gyro Data: X -0.0390954; Y 0.0366519; Z -1.09834
Gyro Data: X -0.0549779; Y -0.0390954; Z -1.13377
Gyro Data: X -0.0390954; Y -0.157603; Z -1.20096
Gyro Data: X -0.0378736; Y -0.0659734; Z -1.20463
Gyro Data: X -0.0390954; Y -0.270002; Z -1.24861
Gyro Data: X -0.0598648; Y -0.0390954; Z -1.29137
Gyro Data: X -0.0830777; Y 0.0390954; Z -1.19485
Gyro Data: X -0.0733038; Y 0.0268781; Z -1.19852
Gyro Data: X -0.0842994; Y 0.123395; Z -1.10322
Gyro Data: X -0.045204; Y 0.0342085; Z -1.0458
Gyro Data: X -0.0610865; Y -0.0219911; Z -0.961502
Gyro Data: X -0.0256563; Y -0.0623083; Z -0.893085
Gyro Data: X -0.0366519; Y -0.0256563; Z -0.894307
Gyro Data: X -0.00366519; Y -0.0158825; Z -0.902859
Gyro Data: X -0.00855211; Y 0.0745256; Z -0.894307
Gyro Data: X -0.0439823; Y 0.100182; Z -0.83322
Gyro Data: X -0.00610865; Y 0.0720821; Z -0.929737
Gyro Data: X -0.00855211; Y 0.0232129; Z -0.901637
Gyro Data: X -0.00610865; Y 0.0500909; Z -0.901637
Gyro Data: X -0.0232129; Y 0; Z -0.933402
Gyro Data: X -0.00244346; Y 0.0781908; Z -0.971276
Gyro Data: X -0.0207694; Y 0.0378736; Z -1.02625
Gyro Data: X 0.013439; Y 0.0415388; Z -1.07757
Gyro Data: X -0.0586431; Y 0.0549779; Z -1.10078
Gyro Data: X -0.031765; Y -0.00610865; Z -1.14965
Gyro Data: X -0.0415388; Y 0.101404; Z -1.14843
Gyro Data: X -0.0610865; Y 0.0476475; Z -1.18752
Gyro Data: X -0.0525344; Y 0.0745256; Z -1.33291
Gyro Data: X -0.0745256; Y 0.135612; Z -1.26938
Gyro Data: X -0.0537561; Y 0.141721; Z -1.30847
Gyro Data: X -0.0549779; Y 0.0342085; Z -1.34513
Gyro Data: X -0.0574213; Y 0.0867429; Z -1.5516
Gyro Data: X -0.06353; Y 0.100182; Z -1.57237
Gyro Data: X -0.0842994; Y 0.0354302; Z -1.53816
Gyro Data: X -0.0830777; Y 0.00610865; Z -1.52594
Gyro Data: X -0.0733038; Y 0.045204; Z -1.51006
Gyro Data: X -0.0232129; Y 0; Z -1.44897
Gyro Data: X -0.00122173; Y 0.0366519; Z -1.43431
Gyro Data: X 0.00733038; Y 0.0244346; Z -1.33413
Gyro Data: X 0.0378736; Y 0.0207694; Z -1.30847
Gyro Data: X 0.00855211; Y 0.0598648; Z -1.30114
Gyro Data: X 0.00855211; Y 0.0427606; Z -1.29259
Gyro Data: X 0.0354302; Y 0.0476475; Z -1.3097
Gyro Data: X 0.0574213; Y 0.0561996; Z -1.3439
Gyro Data: X 0.0745256; Y 0.0476475; Z -1.47463
Gyro Data: X 0.0500909; Y 0.122173; Z -1.53938
Gyro Data: X 0.105069; Y 0.0794125; Z -1.61513
Gyro Data: X 0.118508; Y 0.100182; Z -1.73486
Gyro Data: X 0.109956; Y 0.0806342; Z -1.82771
Gyro Data: X 0.100182; Y 0.144164; Z -1.9792
Gyro Data: X 0.103847; Y 0.11973; Z -2.07205
Gyro Data: X 0.0904081; Y 0.178373; Z -2.15635
Gyro Data: X 0.12706; Y 0.135612; Z -2.23088
Gyro Data: X 0.0537561; Y 0.195477; Z -2.34939
Gyro Data: X 0.0928515; Y 0.174707; Z -2.44957
Gyro Data: X 0.0965167; Y 0.182038; Z -2.48989
Gyro Data: X 0.106291; Y 0.139277; Z -2.41536
Gyro Data: X 0.118508; Y 0.141721; Z -2.44346
Gyro Data: X 0.133169; Y 0.175929; Z -2.2883
Gyro Data: X 0.0720821; Y 0.140499; Z -2.06595
Gyro Data: X 0.0867429; Y 0.150273; Z -1.99631
Gyro Data: X 0.0842994; Y 0.105069; Z -1.85581
Gyro Data: X 0.0977384; Y 0.0928515; Z -1.78739
Gyro Data: X 0.0733038; Y 0.0916298; Z -1.70065
Gyro Data: X 0.108734; Y 0.136834; Z -1.55038
Gyro Data: X 0.0977384; Y 0.103847; Z -1.47219
Gyro Data: X 0.122173; Y 0.0659734; Z -1.44042
Gyro Data: X 0.120951; Y 0.0329867; Z -1.4001
Gyro Data: X 0.108734; Y 0.0537561; Z -1.47707
Gyro Data: X 0.0757473; Y 0.0671952; Z -1.45142
Gyro Data: X 0.117286; Y 0.0305433; Z -1.4673
Gyro Data: X 0.0965167; Y 0.00366519; Z -1.47219
Gyro Data: X 0.0671952; Y 0.0280998; Z -1.48318
Gyro Data: X 0.0916298; Y -0.309098; Z -1.44286
Gyro Data: X 0.0647517; Y -0.0219911; Z -1.49173
Gyro Data: X 0.095295; Y 0.0146608; Z -1.56748
Gyro Data: X 0.0720821; Y -0.00610865; Z -1.47952
Gyro Data: X 0.0488692; Y -0.031765; Z -1.53449
Gyro Data: X 0.0244346; Y -0.0464258; Z -1.6249
Gyro Data: X 0.0146608; Y -0.0232129; Z -1.62612
Gyro Data: X -0.0342085; Y 0.0171042; Z -1.75074
Gyro Data: X -0.00733038; Y 0.0525344; Z -1.87902
Gyro Data: X -0.00122173; Y 0.0513127; Z -1.88391
Gyro Data: X 0.0207694; Y 0.0733038; Z -1.98776
Gyro Data: X -0.00855211; Y 0.0366519; Z -2.09038
Gyro Data: X -0.00610865; Y 0.0415388; Z -2.13681
For me the values do not make much sense and I think there is a lot of noise inthese gyro readings. Maybe because the program always tells me that the gyro bias is being calculated and I should keep the IMU still? The problem is that I am not fully acquainted with the RTIMULib.
Ok, I will revert these changes and report how the imu behaves with all axes left unchanged (using the original sensor readings).
But why is the blue axis pointing downward when the imu faces upward (lying on the table so that I can see both sensor chips)?
And are those negative mag z-values common?
Do you know what the message about the gyro bias calculation tells me?
In the northern hemisphere the Earth’s magnetic field points into the ground.
The force/acceleration due to gravity also points into the ground, but if the accelerometer is not falling, some other force (from the table surface, for example) is pushing up.
Until you fully understand all the coordinate systems involved, and how to interconvert them, it is pointless to try to alter the code. Forget the “colored axis” display program for now. It just adds another complication.
@Jim_Remington thanks for your hints. I got lucky by trying the following:
I not only switched the sign of the z-axis but also of the y-axis to get the original handedness of the coordinate system back. I did this for the accel and the mag and left the gyro axis unchanged.
RTMath::convertToVector(gyroData, m_imuData.gyro, m_gyroScale, false);
RTMath::convertToVector(accelData, m_imuData.accel, m_accelScale, false);
RTMath::convertToVector(compassData, m_imuData.compass, m_compassScale, false);
// sort out gyro axes
m_imuData.gyro.setX(m_imuData.gyro.x());
m_imuData.gyro.setY(m_imuData.gyro.y());
m_imuData.gyro.setZ(m_imuData.gyro.z());
// sort out accel data;
m_imuData.accel.setX(m_imuData.accel.x());
m_imuData.accel.setY(-m_imuData.accel.y());
m_imuData.accel.setZ(-m_imuData.accel.z());
// sort out compass axes
m_imuData.compass.setX(m_imuData.compass.x());
m_imuData.compass.setY(-m_imuData.compass.y());
m_imuData.compass.setZ(-m_imuData.compass.z());
// now do standard processing
handleGyroBias();
calibrateAverageCompass();
calibrateAccel();
// now update the filter
updateFusion();
After I calibrated the compass and accelerometer the rotation about any axis works as expected.
However, as you already noticed, I have to admit that I do not fully understand the code in the library.
I don’t understand why the z-axis of the compass points always downward. Why is the direction independent of the sign of the z-axis in the code or when I turn the imu upside down? I can only think of a part in the code that prevents this from happening and leaves the z-axis pointing into the ground. I noticed, in the mag cal dialog that the z-axis values stay positive or negative (for all orientations), depending on the sign.
I think the library compensates gravity. The calibration manual states that accel values between -1 and 1 for each axis are desired and this is what I get with the accel calibration dialog (see screenshot above).
As far as I understand there are three coordinate systems
gyro
accel
mag
whereas the axes of the accel and mag coordinate systems need to be pointing in the same directions. I am not quite sure about the gyro, but it seems to give me the rotation (rate of change) about each axis of the accel coordinate system and the sign adjusts the rotation direction?
Could you please explain me what gyro bias handling means? And what the code in RTIMU.cpp is doing.
void RTIMU::handleGyroBias()
{
// do axis rotation
if ((m_settings->m_axisRotation > 0) && (m_settings->m_axisRotation < RTIMU_AXIS_ROTATION_COUNT)) {
// need to do an axis rotation
float *matrix = m_axisRotation[m_settings->m_axisRotation];
RTIMU_DATA tempIMU = m_imuData;
// do new x value
if (matrix[0] != 0) {
m_imuData.gyro.setX(tempIMU.gyro.x() * matrix[0]);
m_imuData.accel.setX(tempIMU.accel.x() * matrix[0]);
m_imuData.compass.setX(tempIMU.compass.x() * matrix[0]);
} else if (matrix[1] != 0) {
m_imuData.gyro.setX(tempIMU.gyro.y() * matrix[1]);
m_imuData.accel.setX(tempIMU.accel.y() * matrix[1]);
m_imuData.compass.setX(tempIMU.compass.y() * matrix[1]);
} else if (matrix[2] != 0) {
m_imuData.gyro.setX(tempIMU.gyro.z() * matrix[2]);
m_imuData.accel.setX(tempIMU.accel.z() * matrix[2]);
m_imuData.compass.setX(tempIMU.compass.z() * matrix[2]);
}
// do new y value
if (matrix[3] != 0) {
m_imuData.gyro.setY(tempIMU.gyro.x() * matrix[3]);
m_imuData.accel.setY(tempIMU.accel.x() * matrix[3]);
m_imuData.compass.setY(tempIMU.compass.x() * matrix[3]);
} else if (matrix[4] != 0) {
m_imuData.gyro.setY(tempIMU.gyro.y() * matrix[4]);
m_imuData.accel.setY(tempIMU.accel.y() * matrix[4]);
m_imuData.compass.setY(tempIMU.compass.y() * matrix[4]);
} else if (matrix[5] != 0) {
m_imuData.gyro.setY(tempIMU.gyro.z() * matrix[5]);
m_imuData.accel.setY(tempIMU.accel.z() * matrix[5]);
m_imuData.compass.setY(tempIMU.compass.z() * matrix[5]);
}
// do new z value
if (matrix[6] != 0) {
m_imuData.gyro.setZ(tempIMU.gyro.x() * matrix[6]);
m_imuData.accel.setZ(tempIMU.accel.x() * matrix[6]);
m_imuData.compass.setZ(tempIMU.compass.x() * matrix[6]);
} else if (matrix[7] != 0) {
m_imuData.gyro.setZ(tempIMU.gyro.y() * matrix[7]);
m_imuData.accel.setZ(tempIMU.accel.y() * matrix[7]);
m_imuData.compass.setZ(tempIMU.compass.y() * matrix[7]);
} else if (matrix[8] != 0) {
m_imuData.gyro.setZ(tempIMU.gyro.z() * matrix[8]);
m_imuData.accel.setZ(tempIMU.accel.z() * matrix[8]);
m_imuData.compass.setZ(tempIMU.compass.z() * matrix[8]);
}
}
RTVector3 deltaAccel = m_previousAccel;
deltaAccel -= m_imuData.accel; // compute difference
m_previousAccel = m_imuData.accel;
if ((deltaAccel.length() < RTIMU_FUZZY_ACCEL_ZERO) && (m_imuData.gyro.length() < RTIMU_FUZZY_GYRO_ZERO)) {
// what we are seeing on the gyros should be bias only so learn from this
if (m_gyroSampleCount < (5 * m_sampleRate)) {
m_settings->m_gyroBias.setX((1.0 - m_gyroLearningAlpha) * m_settings->m_gyroBias.x() + m_gyroLearningAlpha * m_imuData.gyro.x());
m_settings->m_gyroBias.setY((1.0 - m_gyroLearningAlpha) * m_settings->m_gyroBias.y() + m_gyroLearningAlpha * m_imuData.gyro.y());
m_settings->m_gyroBias.setZ((1.0 - m_gyroLearningAlpha) * m_settings->m_gyroBias.z() + m_gyroLearningAlpha * m_imuData.gyro.z());
m_gyroSampleCount++;
if (m_gyroSampleCount == (5 * m_sampleRate)) {
// this could have been true already of course
m_settings->m_gyroBiasValid = true;
m_settings->saveSettings();
}
} else {
m_settings->m_gyroBias.setX((1.0 - m_gyroContinuousAlpha) * m_settings->m_gyroBias.x() + m_gyroContinuousAlpha * m_imuData.gyro.x());
m_settings->m_gyroBias.setY((1.0 - m_gyroContinuousAlpha) * m_settings->m_gyroBias.y() + m_gyroContinuousAlpha * m_imuData.gyro.y());
m_settings->m_gyroBias.setZ((1.0 - m_gyroContinuousAlpha) * m_settings->m_gyroBias.z() + m_gyroContinuousAlpha * m_imuData.gyro.z());
}
}
m_imuData.gyro -= m_settings->m_gyroBias;
}
First off, let’s review how data fusion works with a 9-dof IMU to obtain orientation in space. A 9-dof IMU has three sensors, each with different roles:
Gyros. These are really the primary sensors in many ways. They provide virtually instantaneous response to orientation changes with relatively low noise. Since they output angular rates, the outputs have to be (discrete) integrated with respect to time to obtain angle changes. However, gyros have no sense of up or down – they contain no absolute reference. They merely sense orientation changes.
Accelerometers. When using an IMU for orientation sensing, the accelerometers’ role is primarily to track the direction of the gravity vector, since this is known to be straight down. Of course, accelerometers aren’t just sensitive to gravitational acceleration – they sense absolutely every small disturbance and can be inherently noisy. However, when nicely filtered, the outputs can be used to maintain an absolute reference to the horizontal plane – i.e. they provide a reference for pitch and roll. They give no information about yaw (heading). One problem they have is when the IMU is subjected to continuous acceleration (on a moving vehicle under acceleration for example or an aircraft in a turn), the reference will be distorted. It is possible to externally measure the speed of the vehicle and this can be used to correct for these accelerations but it is a potential source of error.
Magnetometers. Magnetometers provide the missing yaw reference by sensing the Earth’s magnetic field. However, what they are really doing is sensing the ambient magnetic field which is affected by almost anything – nearby magnets, magnetic materials, which way the wind is blowing etc.
The task of 9-dof fusion software is to take these three sets of measurements (and instantaneous vehicle speed if corrections are necessary) and come up with a (mostly) correct orientation.
Ok, so that’s orientation. Now, position. To try and sense positional changes using an IMU, you basically have to do two things:
Remove the gravity vector to leave the residual accelerations.
Double integrate the residual accelerations with respect to time to give a change in position.
Simple enough in principle – what could go wrong? Well basically everything. For one thing, this piecewise linear integration which basically assumes a constant acceleration for a time step (sample interval) is probably not going to be exactly correct. Given that the accelerations needs to be double integrated, this can cause significant error. Any error in subtracting the rotated gravity vector to obtain residual accelerations will similarly cause large errors in position. Noise in the accelerometers or incorrect calibration will also lead to errors. Etc etc.
So there are many sources of errors. The problem is that there is no way to correct them. Suppose for example that the IMU is actually at rest but, due to previous errors, a small velocity is left. Even with zero residual accelerations, the code will think that the IMU is still moving and keep changing position accordingly. Basically, the whole thing diverges pretty quickly!
Hi Franz
Would it be possible to convert/or use the RTIMULSM6DS33LIS3MDL.cpp and h files, you created to be able to use in Windows. The RTimulib is stable and works for me.
unfortunately I never tested it with Windows but it should be possible. What have you tried so far? Were you able to build RTIMULib without the files I provided under Windows? Was there a problem building under Windows with the source files from my github repo? Which compiler did you use?
Hi Franz,
I meant to ask if you are planning to convert your work into Windows format. Currently RTimulib does not support LSM6D and LIS3 (minIMU-9 v5).
@kwings714 currently I have not planned that because I don’t have Windows available. However, it should work on Windows too if you are able to compile all the source files using a c++ Windows compiler. I would try MinGW (free) or even Visual Studio and its c++ compiler. And you will probably have to download the Qt Library because the RTIMULib GUI requires it. With that in mind, you could also try to use Qt Creator for Windows (open source).
