insCFGyroscope

Model gyroscope readings for insCF sensor fusion

Since R2026a

Description

The insCFGyroscope object models gyroscope readings for insCF sensor fusion. When creating an insCF filter object, you can specify an insCFGyroscope object to enable the filter to use gyroscope data to predict orientation.

Creation

Syntax

sensor = insCFGyroscope

Description

sensor = insCFGyroscope creates an insCFGyroscope object. You can specify the insCFGyroscope object when creating an insCF object to enable the insCF object to fuse gyroscope data.

Examples

Estimate Orientation from IMU Data Using insCF

Estimate orientation from recorded IMU data by using a complementary filter, represented by an insCF filter object.

Load the rpy_9axis.mat file into the workspace. The file contains recorded accelerometer, gyroscope, and magnetometer sensor data from a device oscillating in pitch (around the y-axis), yaw (around the z-axis), and roll (around the x-axis). The file also contains the sample rate of the recording. Extract the sensor data and sample rate from the loaded workspace variable.

ld = load("rpy_9axis.mat");
accelerometerData = ld.sensorData.Acceleration;
gyroscopeData = ld.sensorData.AngularVelocity;
magnetometerData = ld.sensorData.MagneticField;
imuSampleRate = ld.Fs; % Hz

Create a timetable from the accelerometer, gyroscope, and magnetometer data.

imuDataTT = timetable(accelerometerData,gyroscopeData,magnetometerData,'SampleRate',imuSampleRate,'StartTime',seconds(1/imuSampleRate),'VariableNames',{'Accelerometer', 'Gyroscope', 'Magnetometer'});

Create an insCF filter object using sensor models for accelerometer, magnetometer, and gyroscope. To model orientation, specify insCFMotionOrientation as the motion model.

acc = insCFAccelerometer;
mag = insCFMagnetometer;
gyro = insCFGyroscope;
motionModel = insCFMotionOrientation;
filter = insCF(acc,mag,gyro,motionModel);

Specify the initial orientation of your sensors. You can obtain the initial orientation by using the ecompass function with the accelerometer and magnetometer data at the first time step.

initialOrientation = [0.006727036930852 -0.131203115007920 -0.058108427699335 -0.989628162602834];
stateparts(filter,"Orientation",initialOrientation)

Specify the sensor gains. The sensor gain value must be between 0 and 1.

gainparts(filter,"Accelerometer",0)
gainparts(filter,"Magnetometer",0.01)

Fuse the accelerometer, gyroscope, and magnetometer data using the insCF filter.

outTT = estimateStates(filter,imuDataTT);

Visualize the estimated orientation.

t = outTT.Properties.RowTimes;
plot(t,eulerd(outTT.Orientation,"ZYX","frame"));
title("Orientation Estimate");
legend("Z-rotation","Y-rotation","X-rotation");
xlabel("Time");
ylabel("Degrees");

Estimate Pose and Velocity Using insCF

Estimate pose (orientation and position) and velocity from IMU and GPS data by using a complementary filter, represented by an insCF filter object.

Load the racetrackINSCFDataset.mat file, which contains a timetable with simulated accelerometer, gyroscope, magnetometer, and GPS sensor data for a racetrack-like trajectory, into the workspace. The file also contains the sample rate of the data.

load("raceTrackINSCFDataset.mat")

Create an insCF filter object that includes the accelerometer, gyroscope, magnetometer, and GPS sensor objects, as well as an insCFMotionPose motion model object for modeling pose.

% Sensor object for predicting orientation
gyro = insCFGyroscope;
% Sensor objects for correcting orientation
accel = insCFAccelerometer;
mag = insCFMagnetometer;
% Sensor object for correcting position
gps = insCFGPS(ReferenceLocation=localOrigin);

% Pose motion model
motModel = insCFMotionPose;

% Filter object
filt = insCF(accel,mag,gyro,gps,motModel);

Specify the initial position, velocity, and orientation from the measurement data.

% Set initial position
stateparts(filt,"Position",initPos)
% Set initial orientation
stateparts(filt,"Orientation",initOrient)
% Set initial velocity
stateparts(filt,"Velocity",initVel)

Specify the sensor gains.

% Set Accelerometer gain
gainparts(filt,"Accelerometer",9.6335e-09)
% Set Magnetometer gain
gainparts(filt,"Magnetometer",0.01)
% Set GPS gain
gainparts(filt,"GPS",0.039246)

Fuse the sensor data using the filter. The filter estimates pose (orientation and position) and velocity. Extract the estimated position.

outTT = estimateStates(filt,sensorData);
estPos = outTT.Position;

Calculate the position RMS error.

posErr = truePos - estPos; 
pRMS = sqrt(mean(posErr.^2));
fprintf("Position RMS Error:\tX: %.3f, Y: %.3f, Z: %.3f (meters) \n", pRMS(1),pRMS(2),pRMS(3))

Position RMS Error:	X: 0.372, Y: 0.493, Z: 0.303 (meters) 

Visualize the estimated pose against the ground truth and GPS data.

figure
plot(truePos(:,1),truePos(:,2),".")
hold on
plot(estPos(:,1),estPos(:,2),"r.-")
gpsLocal = lla2ned(sensorData.GPS,localOrigin,"flat");
plot(gpsLocal(:,1),gpsLocal(:,2),".")

title("Pose Estimate")
legend("Ground Truth","Estimated","GPS")

grid on
xlabel("N (m)")
ylabel("E (m)")
axis equal

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2026a