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  • Writer's pictureLloyd Brombach

Accurate Compass Headings: Magnetometer Calibration in C++, Python, and MATLAB

In my last article: Navigating with Magnetometers: Electronic Compass Headings in C++, Python, and MATLAB, we learned how to calculate compass headings by measuring the Earth's magnetic field with a magnetometer. We also learned that magnetometer readings can be distorted by nearby objects, potentially making our compass headings trash. Calibration can help remove some of these distortions to ensure accurate compass headings. In this article, we will learn what calibration can and cannot do, and of course, how to do it with C++, Python, and MATLAB.


What can and cannot be calibrated out

Soft and hard iron distortions occur when the magnetometer is near ferromagnetic materials such as steel or iron, or other magnetic materials. These distortions can cause the magnetic field to be weakened or offset, as I tried to illustrate in figure 1. If these distortions are constant relative to the sensor, they can be measured and removed later. For instance, a sensor and a piece of iron fixed to the same object will always move together, making it possible to remove the distortion caused by the iron.


An image showing two magnetic field diagrams side by side. The left diagram represents an undisturbed magnetic field, while the right diagram shows a distorted magnetic field caused by nearby ferromagnetic materials.
Figure 1. Measuring an undisturbed and a distorted magnetic field.

Unattached metal objects nearby such as furniture or appliances can cause similar distortions but are unpredictable and cannot be easily calibrated out. For this reason, using magnetometer data alone as the source of robot's heading is not a good idea. Instead, Kalman filtering and sensor fusion techniques are often used to improve accuracy.


Saving and Reading Data

Ideally you would save the calibration data to a file, which can then be read by the program that uses magnetometer data until recalibration is needed. The calibration data includes a correction value for each of the sensor's axes that will be applied to the raw magnetometer data to obtain accurate compass headings.


Understanding the Magnetometer Data

Most magnetometers actually have three sensors, each one measuring on its own axis. Figure 2 is a top-down view of sensors measuring a magnetic field. You can see that X and Y axes lay flat and are oriented in the direction of their arrows. The Z axis is harder to picture because it is vertical, pointing up an out of the screen at you. (It is important to check your sensor's documentation because sometimes the Z axis points down). Assuming the sensor and the magnetic field are both perfectly horizontal, try to imagine how much of the field is measured by each axis in the three positions in figure 2.

A Magnetometer electronic compass sensor measuring a magnetic field in different orientations.
Figure 2. Measuring a magnetic field with different sensor orientations.

Let's call position one "straight ahead" and say that position 2 is rotated 180 degrees from position 1. In position 3 the sensor is rotated 90 degrees from position 1.


In positions 1 and 2, both X and Z would measure essentially zero since they are perpendicular to the field direction. The Y axes in both cases will measure the entire field strength but the signs will be opposite. In scenario 3, it is now X that measures the entire field strength while Y measures essentially zero.


As a level magnetometer is rotated, both the X and Y axes pass the same field in the same way. Because of this, we should expect the following of the X and Y data from a flat magnetometer that is rotated in a complete circle:

  • The X and Y measurements should have the same minimum and maximum

  • The measurements should cross zero twice per rotation

  • The midpoint of the range of the measurements should be zero

  • The measurements should be identical, but at different times during the rotation

Figure 3 shows a plot of magnetometer data as a sensor on a robot is rotated several times. The Y values in red mostly adhere to the above expectations, but the X values in blue deviate. The minimum and maximum are not equal and opposite, meaning that zero is not the midpoint. In fact, the X measurements remain below zero for the entire rotation, indicating a distortion along that axis. Fortunately, this is a distortion that can be measured and calibrated out.


Graph showing uncorrected X and Y magnetometer data with a distortion present. The data is plotted as a series of peaks and valleys as the magnetic sensor is rotated. The peaks and valleys are irregular and distorted due to the presence of an external magnetic field.
Figure 3. Uncorrected X and Y magnetometer data in the presence of a distortion.

Do I really need to calibrate if I don't need perfect accuracy?

If you rotate a sensor that is working properly the heading will cycle from -Pi to +Pi, crossing zero in the middle. There will be an abrupt jump as the heading goes from -Pi to +Pi or vice versa. This is normal, expected behavior.

Figure 4 shows the headings in blue that were calculated with the uncorrected data. The headings stay between 1 and 2 radians even though the sensor was being rotated in complete circles and should look more like the data in red. In short, I highly recommend you plan for regular calibration.

Plot of erroneous headings calculated using uncalibrated magnetometer data.
Figure 4. Inaccurate headings generated using good math on uncalibrated magnetometer data.

Calibration Procedure

Keep in mind that calibration needs to be done with the sensor mounted in its final position. You can calibrate it all by itself, but the correction values won't be correct once the distortions of the rest of a robot or whatever are then introduced. To calibrate the magnetometer, we need to follow these steps:

  1. Either start recording all measurements or run a program that saves the highest and lowest values that occur for each axis

  2. Rotate the sensor in one or more (preferably) complete circles about the z axis (

  3. Note the highest and lowest values that occur for each axis

  4. Find the midpoint of the range of values for each axis. For each axis, the midpoint = (min+max)/2

  5. Record these midpoints to a file. - they are now the offsets we apply

  6. Add a code to your heading calculation program to read the file and apply the offsets to each reading

In greater detail, let's look at each step:


Steps 1

Start your sensor streaming data and then your calibration program which, at this step, just needs to save every message to a vector. It is possible to do step 3 on this data live instead of saving it, but the latter gives you more opportunity to check the data for noise and outliers and apply some filter if necessary.


Step 2

Take your robot a flat, open spot outside and slowly rotate it a couple of times. The magnetometer I used is part of the IMU built into a ZED 2i stereo camera I had already mounted on a robot. I wrote a callback function for a ROS subscriber that simply appends each new value to a vector I could have either saved or used on the spot. This is the same data I shared in figure 3.


Step 3

Whether you read it from a file or just read it from the sensor, the goal is to find the lowest value and the highest value measured on each axis. You should end up with 2 variables for each axis.


Step 4

Find the midpoint of the range of values for each axis. For each axis, the midpoint = (min+max)/2


Step 5

Record these midpoints to a file so the heading calculator program can access them later.


Step 6

Apply to live data in your heading calculator program by subtracting the offsets from each and every raw measurement. The result should be data that adheres to the expectations we discussed earlier. Figure 5 shows my recorded data after calibration corrections have been made.

A plot of magnetometer data that has been corrected with calibration
Figure 5. The bad magnetometer data from figure 1 after applying calibration correction.

As you can see, the blue X and red Y data now have equal and opposite minimums and maximums and overall look very much the same. Figure 6 shows the result of performing the heading calculations we learned in the last article on this corrected data.


A plot of compass headings calculated using calibrated magnetometer data.
Figure 6. Compass headings (in blue) closely matching the truth (red) after calibration.

Now the calculated headings appropriately cover the expected range of -pi to +pi and matches our reference data quite accurately!


When to Recalibrate

Recalibration needs to be done when the magnetometer is moved to a different location on the robot (or whatever device you're using it in) or if there are changes to the robot that can affect the magnetic field (did you mount something else near the sensor?). It is also a good idea to recalibrate every few weeks or at least every couple of month, depending on the application's accuracy requirements. If you really need the best accuracy it only takes a minute to calibrate every day.


Let's get to the code

We need two programs: A program to find and save our calibration offsets, and a modified version of the heading calculator that applies the calibration offsets. Lets start with a simple calibration program that runs until 30 seconds has elapsed and some minimum number of samples have been collected. In practice, I prefer to have my program watch the data and look to confirm that 2-3 rotations have been completed.


C++

/*****************************************************
*A sample program to collect and save calibration data
*From the Practical Robotics blog at
*www.lloydbrombach.com/blog
*
*Lloyd Brombach
*Feb 2023
*******************************************************
#include <iostream>
#include <fstream>
#include <vector>
#include <chrono>
#include <thread>

using namespace std;
double getRawDataX();
double getRawDataY();

int main()
{
    // vectors to store the readings until we are ready to save.
    vector<double> xData;
    vector<double> yData;
    const int MIN_DATA_COUNT = 300;
    const int ROTATION_TIME = 30; // 30 seconds for rotation
    int time_elapsed = 0;
    int last_time = time_elapsed;

    // prompt the user to get ready to rotate the device
    cout << "Get ready to rotate the device slowly for two complete rotations in " << ROTATION_TIME << " seconds\n";
    cout << "to gather a minimum of " << MIN_DATA_COUNT << " samples. \n";
    cout << "Press Enter when ready.\n";
    cin.get();

    // start collection
    cout << "GO! " << ROTATION_TIME - time_elapsed << " seconds remaining. ";
    auto start_time = chrono::steady_clock::now(); // start time
    int samples = 0;                               // to count samples

    // collect until time is up AND the minimum number of samples have been collected
    while (time_elapsed < ROTATION_TIME || samples < MIN_DATA_COUNT)
    {
        // get the magnetometer data and save it in the vector
        xData.push_back(getRawDataX());
        yData.push_back(getRawDataY());

        // give a progress update every second
        if (last_time != time_elapsed)
        {
            cout << "\n"
                 << ROTATION_TIME - time_elapsed << " seconds remaining. " << samples << " samples collected.";
            last_time = time_elapsed;
        }
        this_thread::sleep_for(chrono::milliseconds(100)); // wait for 0.1 seconds
        time_elapsed = chrono::duration_cast<chrono::seconds>(chrono::steady_clock::now() - start_time).count();
        samples++; // update count
    }

    // write the collected data to file and find the min and max values
    ofstream outFile("raw_data.dat");
    double minX = xData[0], maxX = xData[0];
    double minY = yData[0], maxY = yData[0];
    for (int i = 0; i < xData.size(); i++)
    {
        outFile << xData[i] << " " << yData[i] << endl;

        // find min and max for each vector
        if (xData[i] < minX)
            minX = xData[i];
        if (xData[i] > maxX)
            maxX = xData[i];
        if (yData[i] < minY)
            minY = yData[i];
        if (yData[i] > maxY)
            maxY = yData[i];
    }
    outFile.close();
    cout << endl
         << "Data collection complete. Saved " << samples << " measurements" << endl;

    // calculate midpoint of min and max for each vector
    double midX = (minX + maxX) / 2.0;
    double midY = (minY + maxY) / 2.0;
    // write midpoint values to calibration file
    ofstream calibFile("calibration_data.txt");
    calibFile << midX << " " << midY << endl;
    calibFile.close();
    cout << "\nCalibration data saved." << endl;
    
    return 0;
}

double getRawDataX()
{
    // replace with code to fetch and return your actual data
    return rand();
}

double getRawDataY()
{
    // replace with code to fetch and return your actual data
    return rand();
}

With the calibration offsets saved to a file, your compass heading calculation program just needs a function to retrieve those values and to subtract those values from the raw data as it comes in.

double offset_x, offset_y; // global variables to store offsets

void read_calibration_data() {
    ifstream calibFile("calibration_data.txt");
    calibFile >> offset_x >> offset_y;
    calibFile.close();
}

Then it's as simple as subtracting those values from the raw measurements:

double corrected_x =  getRawDataX() - offset_x;
double corrected_y =  getRawDataY() - offset_Y;

Python

######################################################
#A sample program to collect and save calibration data
#From the Practical Robotics blog at
#www.lloydbrombach.com/blog
#
#Lloyd Brombach
#Feb 2023
######################################################
import time
import random

def get_raw_data_x():
    # replace with code to fetch and return your actual data
    return random.random()

def get_raw_data_y():
    # replace with code to fetch and return your actual data
    return random.random()

# vectors to store the readings until we are ready to save.
x_data = []
y_data = []
MIN_DATA_COUNT = 300
ROTATION_TIME = 30  # 30 seconds for rotation
time_elapsed = 0
last_time = time_elapsed

# prompt the user to get ready to rotate the device
print(f"Get ready to rotate the device slowly for two complete rotations in {ROTATION_TIME} seconds")
print(f"to gather a minimum of {MIN_DATA_COUNT} samples.")
input("Press Enter when ready.\n")

# start collection
print(f"GO! {ROTATION_TIME - time_elapsed} seconds remaining. ")
start_time = time.monotonic() # start time
samples = 0 # to count samples

# collect until time is up AND the minimum number of samples have been collected
while time_elapsed < ROTATION_TIME or samples < MIN_DATA_COUNT:
    # get the magnetometer data and save it in the vector
    x_data.append(get_raw_data_x())
    y_data.append(get_raw_data_y())

    # give a progress update every second
    if last_time != time_elapsed:
        print(f"\n{ROTATION_TIME - time_elapsed} seconds remaining. {samples} samples collected.", end="")
        last_time = time_elapsed
    time.sleep(0.1) # wait for 0.1 seconds
    time_elapsed = int(time.monotonic() - start_time)
    samples += 1 # update count

# write the collected data to file and find the min and max values
with open("raw_data.dat", "w") as outFile:
    minX, maxX = x_data[0], x_data[0]
    minY, maxY = y_data[0], y_data[0]
    for i in range(len(x_data)):
        outFile.write(str(x_data[i]) + " " + str(y_data[i]) + "\n")

        # find min and max for each vector
        if x_data[i] < minX:
            minX = x_data[i]
        if x_data[i] > maxX:
            maxX = x_data[i]
        if y_data[i] < minY:
            minY = y_data[i]
        if y_data[i] > maxY:
            maxY = y_data[i]

    outFile.close()
    print("\nData collection complete. Saved", len(x_data), "measurements")

    # calculate midpoint of min and max for each vector
    midX = (minX + maxX) / 2.0
    midY = (minY + maxY) / 2.0
    # write midpoint values to calibration file
    with open("calibration_data.txt", "w") as calibFile:
        calibFile.write(str(midX) + " " + str(midY) + "\n")
        calibFile.close()
        print("Calibration data saved.")

With the calibration offsets saved to a file, your compass heading calculation program just needs a function to retrieve those values and to subtract those values from the raw data as it comes in.

offset_x = 0.0
offset_y = 0.0

def read_calibration_data():
    global offset_x, offset_y
    with open("calibration_data.txt", "r") as calibFile:
        offset_x, offset_y = map(float, calibFile.readline().split())
    print("Calibration data read.")

Then it's as simple as subtracting those values from the raw measurements:

corrected_x =  getRawDataX() - offset_x
corrected_y =  getRawDataY() - offset_Y

MATLAB

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%A sample program to collect and save calibration data
%From the Practical Robotics blog at
%www.lloydbrombach.com/blog
%
%Lloyd Brombach
%Feb 2023
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function main()
    % vectors to store the readings until we are ready to save.
    x_data = [];
    y_data = [];
    MIN_DATA_COUNT = 300;
    ROTATION_TIME = 30; % 30 seconds for rotation
    time_elapsed = 0;
    last_time = time_elapsed;

    % prompt the user to get ready to rotate the device
    disp(['Get ready to rotate the device slowly for two complete rotations in ' num2str(ROTATION_TIME) ' seconds']);
    disp(['to gather a minimum of ' num2str(MIN_DATA_COUNT) ' samples.']);
    input('Press Enter when ready.\n', 's');

    % start collection
    disp(['GO! ' num2str(floor(ROTATION_TIME - time_elapsed)) ' seconds remaining. ']);
    start_time = tic; % start time
    samples = 0; % to count samples

    % collect until time is up AND the minimum number of samples have been collected
    while time_elapsed < ROTATION_TIME || samples < MIN_DATA_COUNT
        % get the magnetometer data and save it in the vector
        x_data(end+1) = getRawDataX();
        y_data(end+1) = getRawDataY();

        % give a progress update every second
        if time_elapsed - last_time >= 1
            disp([num2str(floor(ROTATION_TIME - time_elapsed)) ' seconds remaining. ' num2str(samples) ' samples collected.']);
            last_time = time_elapsed;
        end
        pause(0.1); % wait for 0.1 seconds
        time_elapsed = toc(start_time);
        samples = samples + 1; % update count
    end
    minX = x_data(1);
    maxX = x_data(1);
    minY = y_data(1);
    maxY = y_data(1);
for i = 1:samples
    % find min and max for each vector
    if x_data(i) < minX
        minX = x_data(i);
    end
    if x_data(i) > maxX
        maxX = x_data(i);
    end
    if y_data(i) < minY
        minY = y_data(i);
    end
    if y_data(i) > maxY
        maxY = y_data(i);
    end
end

% calculate midpoint of min and max for each vector
midX = (minX + maxX) / 2.0;
midY = (minY + maxY) / 2.0;

% save the data to a .mat file
save('raw_data.mat', 'x_data', 'y_data');
disp(['Data collection complete. Saved ' num2str(samples)]);

% save midpoint values to calibration file
save('calibration_data.mat', 'midX', 'midY'); 
disp('Calibration data saved.');

end

function x = getRawDataX()
    % replace with code to fetch and return your actual data
    x = rand();
end

function y = getRawDataY()
    % replace with code to fetch and return your actual data
    y = rand();
end

With the calibration offsets saved to a file, your compass heading calculation program just needs a function to retrieve those values and to subtract those values from the raw data as it comes in. MATLAB makes this ridiculously easy.

function [offset_x, offset_y] = read_calibration_data()
    % Load calibration data from .mat file
    load('calibration_data.mat', 'offset_x', 'offset_y');
end

Then it's as simple as subtracting those values from the raw measurements:

corrected_x =  getRawDataX() - offset_x;
corrected_y =  getRawDataY() - offset_Y;

Conclusion

With parts 1 and 2 in this series behind us, we can now calculate accurate headings even when the magnetic field measurements are corrupted by nearby disturbances, but there is one more factor that can ruin our calculations – tilt. If the sensor is not fairly level, the X and Y measurements cannot be trusted. In the next article in this series, I willattempt to explain why and what we can do about it.

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