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how can I use insfilterAsync?

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Federico Midei
Federico Midei on 9 Dec 2021
Answered: arushi on 30 Apr 2024
Hi, I'm a student and for a project work I'm trying to apply the sensor fusion to my GPS and IMU measurements. I've found on the mathworks help center the Pose Estimation From Asynchronous Sensors example and I think is exactly what I need to do.
The question is, in the example uses ground truth and generated sensors measurements. Since I've raw data coming from a .csv file where I've arranged in different vectors the acc gyr mag and GPS(lat & long) measurements how can I implement the filter?
Reading the code in the example is not really clear to me where to substitute the values coming from my measurements.
The main problem is that is not clear to me where in the code the filter takes the logged file as inputs.
Any kind of help is welcome!
Here what I've thought wold work in my case:
%% load IMUdata.mat / load GPSdata.mat
%datas file are matrices composed by:
%accelerometer: ACCx_vec ACCy_vec ACCz_vec
%gyroscope: GYRx_vec GYRy_vec GYRz_vec
%magnetometer: MAGx_vec MAGy_vec MAGz_vec
%GPS : gpsLAT_vec gpsLONG_vec
refloc = [current_location_lat current_location_long current_location_height];
%% initialize insfilterMARG filter obj
f = insfilterMARG;
f.IMUSampleRate = imuFs;
f.ReferenceLocation = refloc;
f.AccelerometerBiasNoise = 2e-4;
f.AccelerometerNoise = 2;
f.GyroscopeBiasNoise = 1e-16;
f.GyroscopeNoise = 1e-5; %NB these data has to be corrected/confirmed reading the sensors datasheet
f.MagnetometerBiasNoise = 1e-10;
f.GeomagneticVectorNoise = 1e-12;
f.StateCovariance = 1e-9*ones(22);
f.State = initstate;
gpsidx = 1;
N = size(a,1);
q = zeros(N,1,'quaternion');
%% sensor fusion (MARG and GPS)
for ii = 1:size(a,1) % Fuse IMU
f.predict(a(ii,:), w(ii,:));
if ~mod(ii,fix(imuFs/2)) % Fuse magnetometer at 1/2 the IMU rate
if ~mod(ii,imuFs) % Fuse GPS once per second
gpsidx = gpsidx + 1;
[p(ii,:),q(ii)] = pose(f); %Log estimated pose
%% calculate and estimate RMS(root mean squared) error
posErr = truePos - p;
qErr = rad2deg(dist(trueOrient,q));
pRMS = sqrt(mean(posErr.^2));
qRMS = sqrt(mean(qErr.^2));
fprintf('Position RMS Error\n\tX: %.2f, Y: %.2f, Z: %.2f (meters)\n\n',pRMS(1),pRMS(2),pRMS(3));
fprintf('Quaternion Distance RMS Error\n\t%.2f (degrees)\n\n',qRMS);
%% plot results
legend('x acc','y acc','z acc')
ylabel('acc. in m/s^2')
legend('x rot','y rot','z rot')
ylabel('vel in rad/s')
ylabel('Magnetic Field in \muT')
ylabel(' ')
Here the example code:
%% Pose Estimation From Asynchronous Sensors
% This example shows how you might fuse sensors at different rates to
% estimate pose. Accelerometer, gyroscope, magnetometer and GPS are used
% to determine orientation and position of a vehicle moving along a
% circular path. You can use controls on the figure window to vary sensor
% rates and experiment with sensor dropout while seeing the effect on the
% estimated pose.
% Copyright 2018-2019 The MathWorks, Inc.
%% Simulation Setup
% Load prerecorded sensor data. The sensor data is based on a circular
% trajectory created using the |waypointTrajectory| class. The sensor
% values were created using the |gpsSensor| and |imuSensor| classes. The
% |CircularTrajectorySensorData.mat| file used here can be generated with
% the |generateCircularTrajSensorData| function.
ld = load('CircularTrajectorySensorData.mat');
Fs = ld.Fs; % maximum MARG rate
gpsFs = ld.gpsFs; % maximum GPS rate
ratio = Fs./gpsFs;
refloc = ld.refloc;
trajOrient = ld.trajData.Orientation;
trajVel = ld.trajData.Velocity;
trajPos = ld.trajData.Position;
trajAcc = ld.trajData.Acceleration;
trajAngVel = ld.trajData.AngularVelocity;
accel = ld.accel;
gyro = ld.gyro;
mag = ld.mag;
lla = ld.lla;
gpsvel = ld.gpsvel;
%% Fusion Filter
% Create an |insfilterAsync| to fuse IMU + GPS measurements. This fusion
% filter uses a continuous-discrete extended Kalman filter (EKF) to track
% orientation (as a quaternion), angular velocity, position, velocity,
% acceleration, sensor biases, and the geomagnetic vector.
% This |insfilterAsync| has several methods to process sensor data:
% |fuseaccel|, |fusegyro|, |fusemag| and |fusegps|. Because
% |insfilterAsync| uses a continuous-discrete EKF, the |predict| method can
% step the filter forward an arbitrary amount of time.
fusionfilt = insfilterAsync('ReferenceLocation', refloc);
%% Initialize the State Vector of the |insfilterAsync|
% The |insfilterAsync| tracks the pose states in a 28-element vector.
% The states are:
% States Units Index
% Orientation (quaternion parts) 1:4
% Angular Velocity (XYZ) rad/s 5:7
% Position (NED) m 8:10
% Velocity (NED) m/s 11:13
% Acceleration (NED) m/s^2 14:16
% Accelerometer Bias (XYZ) m/s^2 17:19
% Gyroscope Bias (XYZ) rad/s 20:22
% Geomagnetic Field Vector (NED) uT 23:25
% Magnetometer Bias (XYZ) uT 26:28
% Ground truth is used to help initialize the filter states, so the filter
% converges to good answers quickly.
Nav = 100;
initstate = zeros(28,1);
initstate(1:4) = compact( meanrot(trajOrient(1:Nav)));
initstate(5:7) = mean( trajAngVel(10:Nav,:), 1);
initstate(8:10) = mean( trajPos(1:Nav,:), 1);
initstate(11:13) = mean( trajVel(1:Nav,:), 1);
initstate(14:16) = mean( trajAcc(1:Nav,:), 1);
initstate(23:25) = ld.magField;
% The gyroscope bias initial value estimate is low for the Z-axis. This is
% done to illustrate the effects of fusing the magnetometer in the
% simulation.
initstate(20:22) = deg2rad([3.125 3.125 3.125]);
fusionfilt.State = initstate;
%% Set the Process Noise Values of the |insfilterAsync|
% The process noise variance describes the uncertainty of the motion model
% the filter uses.
fusionfilt.QuaternionNoise = 1e-2;
fusionfilt.AngularVelocityNoise = 100;
fusionfilt.AccelerationNoise = 100;
fusionfilt.MagnetometerBiasNoise = 1e-7;
fusionfilt.AccelerometerBiasNoise = 1e-7;
fusionfilt.GyroscopeBiasNoise = 1e-7;
%% Define the Measurement Noise Values Used to Fuse Sensor Data
% Each sensor has some noise in the measurements. These values can
% typically be found on a sensor's datasheet.
Rmag = 0.4;
Rvel = 0.01;
Racc = 610;
Rgyro = 0.76e-5;
Rpos = 3.4;
fusionfilt.StateCovariance = diag(1e-3*ones(28,1));
%% Initialize Scopes
% The |HelperScrollingPlotter| scope enables plotting of variables
% over time. It is used here to track errors in pose. The
% |PoseViewerWithSwitches| scope allows 3D visualization of the filter
% estimate and ground truth pose. The scopes can slow the simulation. To
% disable a scope, set the corresponding logical variable to false.
useErrScope = true; % Turn on the streaming error plot.
usePoseView = true; % Turn on the 3D pose viewer.
if usePoseView
posescope = PoseViewerWithSwitches(...
'XPositionLimits', [-30 30], ...
'YPositionLimits', [-30, 30], ...
'ZPositionLimits', [-10 10]);
f = gcf;
if useErrScope
errscope = HelperScrollingPlotter(...
'NumInputs', 4, ...
'TimeSpan', 10, ...
'SampleRate', Fs, ...
'YLabel', {'degrees', ...
'meters', ...
'meters', ...
'meters'}, ...
'Title', {'Quaternion Distance', ...
'Position X Error', ...
'Position Y Error', ...
'Position Z Error'}, ...
'YLimits', ...
[ -1, 30
-2, 2
-2 2
-2 2]);
%% Simulation Loop
% The simulation of the fusion algorithm allows you to inspect the effects
% of varying sensor sample rates. Further, fusion of individual sensors
% can be prevented by unchecking the corresponding checkbox. This can be
% used to simulate sensor dropout.
% Some configurations produce dramatic results. For example, turning
% off the GPS sensor causes the position estimate to drift quickly.
% Turning off the magnetometer sensor will cause the orientation estimate
% to slowly deviate from the ground truth as the estimate rotates too
% fast. Conversely, if the gyroscope is turned off and the magnetometer is
% turned on, the estimated orientation shows a wobble and lacks the
% smoothness present if both sensors are used.
% Turning all sensors on but setting them to run at the lowest rate
% produces an estimate that visibly deviates from the ground truth and
% then snaps back to a more correct result when sensors are fused. This is
% most easily seen in the |HelperScrollingPlotter| of the running estimate
% errors.
% The main simulation runs at 100 Hz. Each iteration inspects the
% checkboxes on the figure window and, if the sensor is enabled,
% fuses the data for that sensor at the appropriate rate.
for ii=1:size(accel,1)
% Fuse Accelerometer
if (f.UserData.Accelerometer) && ...
mod(ii, fix(Fs/f.UserData.AccelerometerSampleRate)) == 0
fusionfilt.fuseaccel(accel(ii,:), Racc);
% Fuse Gyroscope
if (f.UserData.Gyroscope) && ...
mod(ii, fix(Fs/f.UserData.GyroscopeSampleRate)) == 0
fusionfilt.fusegyro(gyro(ii,:), Rgyro);
% Fuse Magnetometer
if (f.UserData.Magnetometer) && ...
mod(ii, fix(Fs/f.UserData.MagnetometerSampleRate)) == 0
fusionfilt.fusemag(mag(ii,:), Rmag);
% Fuse GPS
if (f.UserData.GPS) && mod(ii, fix(Fs/f.UserData.GPSSampleRate)) == 0
fusionfilt.fusegps(lla(ii,:), Rpos, gpsvel(ii,:), Rvel);
% Plot the pose error
[p,q] = pose(fusionfilt);
posescope(p, q, trajPos(ii,:), trajOrient(ii));
orientErr = rad2deg(dist(q, trajOrient(ii) ));
posErr = p - trajPos(ii,:);
errscope(orientErr, posErr(1), posErr(2), posErr(3));
%% Conclusion
% The |insfilterAsync| allows for various and varying sample rates. The
% quality of the estimated outputs depends heavily on individual sensor
% fusion rates. Any sensor dropout will have a profound effect on the
% output.

Answers (1)

arushi on 30 Apr 2024
Hi Federico,
To adapt the Pose Estimation From Asynchronous Sensors example for your own GPS and IMU data from a `.csv` file, you'll need to make several modifications to the example code. The key steps involve loading your data, initializing the filter with appropriate parameters based on your sensors, and then modifying the sensor fusion loop to use your data.
1. Load Your Data
First, you need to load your data from the `.csv` file. Assuming you have already arranged your data into vectors (`ACCx_vec`, `ACCy_vec`, `ACCz_vec`, `GYRx_vec`, `GYRy_vec`, `GYRz_vec`, `MAGx_vec`, `MAGy_vec`, `MAGz_vec`, `gpsLAT_vec`, `gpsLONG_vec`), you can load them directly into MATLAB. If not, you'll need to use `readtable`, `readmatrix`, or similar functions to read your CSV data into MATLAB.
% Example of loading CSV data
data = readtable('your_data_file.csv');
% Extracting specific columns, assuming you know their names or indices
ACCx_vec = data.ACCx;
ACCy_vec = data.ACCy;
ACCz_vec = data.ACCz;
% Continue for other sensors
2. Initialize the Filter
You've already outlined how to initialize the `insfilterMARG` object and set its properties. Make sure the sample rate (`imuFs`) and reference location (`refloc`) accurately reflect your setup.
3. Sensor Fusion Loop
The main loop you've proposed seems to be on the right track, but you need to ensure the variables 'a', 'w', 'm', 'lla', 'Rmag', 'Rpos', 'gpsvel', and 'Rvel' are correctly defined and populated with your data. The variables 'a', 'w', and 'm' should be your accelerometer, gyroscope, and magnetometer measurements, respectively. The 'lla' variable should contain your GPS latitude, longitude, and altitude data. 'Rmag', 'Rpos', 'gpsvel' and 'Rvel' are the noise covariance matrices for the magnetometer, GPS position, GPS velocity, and velocity noise, respectively.
Hope this helps.




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