Part of what I want to do is fuse gyroscope and accelerometer data to get an orientation estimate. When I fuse the data using the imufilter() I get results looking very similar to what I am expecting, without majorly tweaking the noise settings and so on.
I then proceeded to try to fuse it using the insEKF() because I eventually also need to fuse GPS measurements. For the first part I only added the gyroscope and the accelerometer to see the orientation estimate. I coded it very similar to the example in the documentation but for some reason the estimated orientation just start spinning and jumping around "uncontrollably".
Heres part of how Ive implemented it:
I also tried tuning it using the data I got from the other filter but that not seems like the issue. Also fusing the data sequentially using fuse() didnt change anything.
…

gyrofix=[gyro(:,2) gyro(:,3) gyro(:,1)]; %data already in workspace
acclfix=[-accl(:,2) -accl(:,3) -accl(:,1)];

option = insOptions(ReferenceFrame="ENU");
insAccel=insAccelerometer;
insGyro=insGyroscope;

tt_meas=timetable(acclfix,gyrofix,’RowTimes’, seconds(0:0.01:(size(acclfix,1)/100)-0.01))
tt_meas.Properties.VariableNames={‘Accelerometer’ ‘Gyroscope’}
tt_meas.Accelerometer(:,:)=0;

filt = insEKF(insAccel, insGyro, option);

initOrient=quaternion(1, 0, 0, 0);
stateparts(filt,"Orientation",compact(initOrient));
statecovparts(filt,"Orientation",0.01);

mnoise=tunernoise(filt);
mnoise.AccelerometerNoise=0.0002;
mnoise.GyroscopeNoise=9e-5;
untunedEst = estimateStates(filt,tt_meas,mnoise);Part of what I want to do is fuse gyroscope and accelerometer data to get an orientation estimate. When I fuse the data using the imufilter() I get results looking very similar to what I am expecting, without majorly tweaking the noise settings and so on.
I then proceeded to try to fuse it using the insEKF() because I eventually also need to fuse GPS measurements. For the first part I only added the gyroscope and the accelerometer to see the orientation estimate. I coded it very similar to the example in the documentation but for some reason the estimated orientation just start spinning and jumping around "uncontrollably".
Heres part of how Ive implemented it:
I also tried tuning it using the data I got from the other filter but that not seems like the issue. Also fusing the data sequentially using fuse() didnt change anything.
…

gyrofix=[gyro(:,2) gyro(:,3) gyro(:,1)]; %data already in workspace
acclfix=[-accl(:,2) -accl(:,3) -accl(:,1)];

option = insOptions(ReferenceFrame="ENU");
insAccel=insAccelerometer;
insGyro=insGyroscope;

tt_meas=timetable(acclfix,gyrofix,’RowTimes’, seconds(0:0.01:(size(acclfix,1)/100)-0.01))
tt_meas.Properties.VariableNames={‘Accelerometer’ ‘Gyroscope’}
tt_meas.Accelerometer(:,:)=0;

filt = insEKF(insAccel, insGyro, option);

initOrient=quaternion(1, 0, 0, 0);
stateparts(filt,"Orientation",compact(initOrient));
statecovparts(filt,"Orientation",0.01);

mnoise=tunernoise(filt);
mnoise.AccelerometerNoise=0.0002;
mnoise.GyroscopeNoise=9e-5;
untunedEst = estimateStates(filt,tt_meas,mnoise); Part of what I want to do is fuse gyroscope and accelerometer data to get an orientation estimate. When I fuse the data using the imufilter() I get results looking very similar to what I am expecting, without majorly tweaking the noise settings and so on.
I then proceeded to try to fuse it using the insEKF() because I eventually also need to fuse GPS measurements. For the first part I only added the gyroscope and the accelerometer to see the orientation estimate. I coded it very similar to the example in the documentation but for some reason the estimated orientation just start spinning and jumping around "uncontrollably".
Heres part of how Ive implemented it:
I also tried tuning it using the data I got from the other filter but that not seems like the issue. Also fusing the data sequentially using fuse() didnt change anything.
…

gyrofix=[gyro(:,2) gyro(:,3) gyro(:,1)]; %data already in workspace
acclfix=[-accl(:,2) -accl(:,3) -accl(:,1)];

option = insOptions(ReferenceFrame="ENU");
insAccel=insAccelerometer;
insGyro=insGyroscope;

tt_meas=timetable(acclfix,gyrofix,’RowTimes’, seconds(0:0.01:(size(acclfix,1)/100)-0.01))
tt_meas.Properties.VariableNames={‘Accelerometer’ ‘Gyroscope’}
tt_meas.Accelerometer(:,:)=0;

filt = insEKF(insAccel, insGyro, option);

initOrient=quaternion(1, 0, 0, 0);
stateparts(filt,"Orientation",compact(initOrient));
statecovparts(filt,"Orientation",0.01);

mnoise=tunernoise(filt);
mnoise.AccelerometerNoise=0.0002;
mnoise.GyroscopeNoise=9e-5;
untunedEst = estimateStates(filt,tt_meas,mnoise); sensor fusion, inertial, filter, ekf, insekf MATLAB Answers — New Questions

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