LSM303D Calibration and Tilt Compensated

[PYJTER]

Hi everyone ... I'm trying to calibrate my compass LSM303D by of this description:

LSM303DLHC - Calibration, Pitch, Roll and Tilt Compensated Heading

I used my own slightly modified version of Magneto 1.2. You can download the C code here: https://forum.pololu.com/t/correcting-the-balboa-magnetometer/14315 The raw data that you supplied (after removing a bad character "J" from an X coordinate) were corrected by magneto using the following transformation: Magneto 1.2 Input .csv file? magRaw.csv Average magnitude of 1917 vectors (default Hm) = 7232.985931 Expected norm of local field vector Hm? (0 for default above) 512. Combined bias vect…

This is my first time doing this, so forgive my questions.

I used this code:

#include <Wire.h>
#include <LSM303.h>
LSM303 compass;

void setup()
{
Serial.begin(9600);
Wire.begin();
compass.init();
compass.enableDefault();
Serial.println("Magnetometer Uncalibrated (Units in Nanotesla)"); 
}

void loop()
{
compass.read();
float Xm_print, Ym_print, Zm_print;

Xm_print = compass.m.x*(100000.0/1100.0); // Gain X [LSB/Gauss] for selected sensor input field range (1.3 in these case)
Ym_print = compass.m.y*(100000.0/1100.0); // Gain Y [LSB/Gauss] for selected sensor input field range
Zm_print = compass.m.z*(100000.0/980.0 );  // Gain Z [LSB/Gauss] for selected sensor input field range

Serial.print(Xm_print, 10); Serial.print(" "); Serial.print(Ym_print, 10); Serial.print(" "); Serial.println(Zm_print, 10);
delay(100);
}

I move the compass on the table very slowly in all directions. And now the question is whether I should make movements similar to 8 in the air with the compass? Will it suffice that I rotate it around 360 degrees on the table several times.

And I got the raw data

raw_mag_1.zip (9.8 KB)

I set everything in the magnet and click calibrate:

Then I paste the data from the first part into the code below

#include <Wire.h>
#include <LSM303.h>
LSM303 compass;

void setup()
{
Serial.begin(9600);
Wire.begin();
compass.init();
compass.enableDefault();
Serial.println("Magnetometer Calibrated (Units in Nanotesla)"); 
}

void loop()
{
compass.read();
float Xm_off, Ym_off, Zm_off, Xm_cal, Ym_cal, Zm_cal;

Xm_off = compass.m.x*(100000.0/1100.0) + 3468.773708; //X-axis combined bias (Non calibrated data - bias)
Ym_off = compass.m.y*(100000.0/1100.0) +13493.601503; //Y-axis combined bias (Default: substracting bias)
Zm_off = compass.m.z*(100000.0/980.0 ) -132660.251821; //Z-axis combined bias

Xm_cal =  0.253088*Xm_off + 0.000019*Ym_off -0.016298*Zm_off; //X-axis correction for combined scale factors (Default: positive factors)
Ym_cal = 0.000019*Xm_off + 0.254566*Ym_off -0.016239*Zm_off; //Y-axis correction for combined scale factors
Zm_cal =  -0.016298*Xm_off -0.016239*Ym_off + 0.350500*Zm_off; //Z-axis correction for combined scale factors

Serial.print(Xm_cal, 10); Serial.print(" "); Serial.print(Ym_cal, 10); Serial.print(" "); Serial.println(Zm_cal, 10);
delay(125);
}

I collect raw data again
raw_mag_2.zip (10.2 KB)

Imports to the magneto

And the next question is, is the data correct ?? Am I doing something wrong? Thank you in advance for your help, this is all new to me so I can make mistakes.

[jremington]

@PYJTER

I collect raw data again
raw_mag_2.zip (10.2 KB)

Why do you collect the "raw data" a second time? If you want to know that the corrections have been applied properly, run Magneto on the corrected data.

Remove the incorrect scale factors in this code:

Xm_print = compass.m.x*(100000.0/1100.0); // Gain X [LSB/Gauss] for selected sensor input field range (1.3 in these case)
Ym_print = compass.m.y*(100000.0/1100.0); // Gain Y [LSB/Gauss] for selected sensor input field range
Zm_print = compass.m.z*(100000.0/980.0 );  // Gain Z [LSB/Gauss] for selected sensor input field range

The raw data are these, and it is to these data that you apply the corrections from Magneto. Similarly for the accelerometer.

Xm_raw = compass.m.x;
Ym_raw = compass.m.y;
Zm_raw = compass.m.z;

[PYJTER]

Ok i remove the incorrect scale

I collect raw data and put to magneto

I applied the correction factors and collected the data into the magneto.

For accelerometer raw data:

And with corrections

Are the data corrected correctly ?? Thank you in advance for your help

[jremington]

@PYJTER

Are the data corrected correctly ??

No. The bias and diagonal scale factor elements should be about 0.0 and 1.00 after correction. Post your code, using code tags. Please also post the raw data, before correction. You can append the files to the post.

[PYJTER]

I did everything thoroughly once again.
First, I ran this program and collected the data

#include <Wire.h>
#include <LSM303.h>
LSM303 compass;

void setup()
{
Serial.begin(9600);
Wire.begin();
compass.init();
compass.enableDefault();
Serial.println("Magnetometer Uncalibrated (Units in Nanotesla)"); 
}

void loop()
{
compass.read();
float Xm_print, Ym_print, Zm_print;

Xm_print = compass.m.x; // Gain X [LSB/Gauss] for selected sensor input field range (1.3 in these case)
Ym_print = compass.m.y; // Gain Y [LSB/Gauss] for selected sensor input field range
Zm_print = compass.m.z;  // Gain Z [LSB/Gauss] for selected sensor input field range

Serial.print(Xm_print, 10); Serial.print(" "); Serial.print(Ym_print, 10); Serial.print(" "); Serial.println(Zm_print, 10);
delay(50);
}

Raw data:
raw_mag_1.zip (6.6 KB)

Magneto:

Now it corrects my code by the coefficients

#include <Wire.h>
#include <LSM303.h>
LSM303 compass;

void setup()
{
Serial.begin(9600);
Wire.begin();
compass.init();
compass.enableDefault();
Serial.println("Magnetometer Calibrated (Units in Nanotesla)"); 
}

void loop()
{
compass.read();
float Xm_off, Ym_off, Zm_off, Xm_cal, Ym_cal, Zm_cal;

Xm_off = compass.m.x -34.487997; //X-axis combined bias (Non calibrated data - bias)
Ym_off = compass.m.y +67.928330; //Y-axis combined bias (Default: substracting bias)
Zm_off = compass.m.z +161.429010; //Z-axis combined bias

Xm_cal =  17.103591*Xm_off + 0.241650*Ym_off + 0.091792*Zm_off; //X-axis correction for combined scale factors (Default: positive factors)
Ym_cal =  0.241650*Xm_off + 17.157825*Ym_off + 0.383122*Zm_off; //Y-axis correction for combined scale factors
Zm_cal =  0.091792*Xm_off + 0.383122*Ym_off + 17.255100*Zm_off; //Z-axis correction for combined scale factors

Serial.print(Xm_cal, 10); Serial.print(" "); Serial.print(Ym_cal, 10); Serial.print(" "); Serial.println(Zm_cal, 10);
delay(50);
}

Once again I am collecting the corrected data already

correct_mag_2.zip (12.8 KB)

And magneto with corrected data:

[jremington]

@PYJTER

Xm_off = compass.m.x -34.487997; //X-axis combined bias (Non calibrated data - bias)
Ym_off = compass.m.y +67.928330; //Y-axis combined bias (Default: substracting bias)
Zm_off = compass.m.z +161.429010; //Z-axis combined bias

Wrong signs. The offsets are subtractive, not additive.

Also, remove the ", 10" in these lines. Most of those 10 digits are nonsense.

Serial.print(Xm_print, 10)

[PYJTER]

I correct code:

Xm_off = compass.m.x +34.487997;
Ym_off = compass.m.y -67.928330;
Zm_off = compass.m.z -161.429010;

And magneto with corrected data:

And the diagonal is [1.035 , 0.999 , 1.028 ] , approximately equal to ‘1’

Is it correct now ??

The offsets are subtractive

Which means subtractive?

I have subtract all the values ??

Xm_off = compass.m.x -34.487997;
Ym_off = compass.m.y -67.928330;
Zm_off = compass.m.z -161.429010;

I don't know what to do now or should I subtract these values ??

[jremington]

No, there is something very wrong. Please post the raw magnetometer data (append a .txt file to your post) and I will take a look at it.

Subtractive means you subtract the values. To subtract -34, you add 34.

[PYJTER]

Txt files cannot be attached

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-925.0000000000 -938.0000000000 -2317.0000000000
-925.0000000000 -938.0000000000 -2317.0000000000
-925.0000000000 -938.0000000000 -2317.0000000000
-486.0000000000 -1081.0000000000 -2349.0000000000
-486.0000000000 -1081.0000000000 -2349.0000000000
-486.0000000000 -1081.0000000000 -2349.0000000000
-479.0000000000 -989.0000000000 -2387.0000000000
-479.0000000000 -989.0000000000 -2387.0000000000
-479.0000000000 -989.0000000000 -2387.0000000000
-863.0000000000 -1077.0000000000 -2259.0000000000
-863.0000000000 -1077.0000000000 -2259.0000000000
-863.0000000000 -1077.0000000000 -2259.0000000000
-717.0000000000 -1127.0000000000 -2285.0000000000
-717.0000000000 -1127.0000000000 -2285.0000000000
-717.0000000000 -1127.0000000000 -2285.0000000000
-717.0000000000 -1127.0000000000 -2290.0000000000
-624.0000000000 -1151.0000000000 -2290.0000000000
-624.0000000000 -1151.0000000000 -2290.0000000000
-624.0000000000 -1151.0000000000 -2290.0000000000
-563.0000000000 -1141.0000000000 -2319.0000000000
-563.0000000000 -1141.0000000000 -2319.0000000000
-563.0000000000 -1141.0000000000 -2319.0000000000
-408.0000000000 -1171.0000000000 -2331.0000000000
-408.0000000000 -1171.0000000000 -2331.0000000000
-408.0000000000 -1171.0000000000 -2331.0000000000
-156.0000000000 -1194.0000000000 -2356.0000000000
-156.0000000000 -1194.0000000000 -2356.0000000000
-156.0000000000 -1194.0000000000 -2356.0000000000
80.0000000000 -1207.0000000000 -2366.0000000000
80.0000000000 -1207.0000000000 -2366.0000000000
80.0000000000 -1207.0000000000 -2366.0000000000
232.0000000000 -1131.0000000000 -2411.0000000000
232.0000000000 -1131.0000000000 -2411.0000000000
232.0000000000 -1131.0000000000 -2411.0000000000
352.0000000000 -1099.0000000000 -2422.0000000000
352.0000000000 -1099.0000000000 -2422.0000000000
352.0000000000 -1099.0000000000 -2422.0000000000
628.0000000000 -1236.0000000000 -2306.0000000000
628.0000000000 -1236.0000000000 -2306.0000000000
628.0000000000 -1236.0000000000 -2306.0000000000
620.0000000000 -1144.0000000000 -2358.0000000000
620.0000000000 -1144.0000000000 -2358.0000000000
620.0000000000 -1144.0000000000 -2358.0000000000
579.0000000000 -955.0000000000 -2475.0000000000
579.0000000000 -955.0000000000 -2475.0000000000
579.0000000000 -955.0000000000 -2475.0000000000
554.0000000000 -961.0000000000 -2492.0000000000
554.0000000000 -961.0000000000 -2492.0000000000
554.0000000000 -961.0000000000 -2492.0000000000
722.0000000000 -1208.0000000000 -2364.0000000000
722.0000000000 -1208.0000000000 -2364.0000000000
722.0000000000 -1208.0000000000 -2364.0000000000
1016.0000000000 -1264.0000000000 -2268.0000000000
1016.0000000000 -1264.0000000000 -2268.0000000000
1016.0000000000 -1264.0000000000 -2268.0000000000
1016.0000000000 -1264.0000000000 -2268.0000000000
1152.0000000000 -1223.0000000000 -2263.0000000000
1152.0000000000 -1223.0000000000 -2263.0000000000
1152.0000000000 -1223.0000000000 -2263.0000000000
1125.0000000000 -1249.0000000000 -2260.0000000000
1125.0000000000 -1249.0000000000 -2260.0000000000
1125.0000000000 -1249.0000000000 -2260.0000000000
1155.0000000000 -1293.0000000000 -2230.0000000000
1155.0000000000 -1293.0000000000 -2230.0000000000
1155.0000000000 -1293.0000000000 -2230.0000000000
1276.0000000000 -1360.0000000000 -2112.0000000000
1276.0000000000 -1360.0000000000 -2112.0000000000
1276.0000000000 -1360.0000000000 -2112.0000000000
1239.0000000000 -1437.0000000000 -2062.0000000000
1239.0000000000 -1437.0000000000 -2062.0000000000
1239.0000000000 -1437.0000000000 -2062.0000000000
1319.0000000000 -1527.0000000000 -1955.0000000000
1319.0000000000 -1527.0000000000 -1955.0000000000
1319.0000000000 -1527.0000000000 -1955.0000000000
1289.0000000000 -1549.0000000000 -1947.0000000000
1289.0000000000 -1549.0000000000 -1947.0000000000
1289.0000000000 -1549.0000000000 -1947.0000000000
1396.0000000000 -1530.0000000000 -1909.0000000000
1396.0000000000 -1530.0000000000 -1909.0000000000
1396.0000000000 -1530.0000000000 -1909.0000000000
1376.0000000000 -1497.0000000000 -1963.0000000000
1376.0000000000 -1497.0000000000 -1963.0000000000
1376.0000000000 -1497.0000000000 -1963.0000000000
1461.0000000000 -1242.0000000000 -2072.0000000000
1461.0000000000 -1242.0000000000 -2072.0000000000
1461.0000000000 -1242.0000000000 -2072.0000000000
1386.0000000000 -879.0000000000 -2299.0000000000
1386.0000000000 -879.0000000000 -2299.0000000000
1386.0000000000 -879.0000000000 -2299.0000000000
1262.0000000000 -826.0000000000 -2384.0000000000
1262.0000000000 -826.0000000000 -2384.0000000000
1262.0000000000 -826.0000000000 -2384.0000000000
1262.0000000000 -826.0000000000 -2384.0000000000
1012.0000000000 -760.0000000000 -2523.0000000000
1012.0000000000 -760.0000000000 -2523.0000000000
1012.0000000000 -760.0000000000 -2523.0000000000
976.0000000000 -969.0000000000 -2466.0000000000
976.0000000000 -969.0000000000 -2466.0000000000
976.0000000000 -969.0000000000 -2466.0000000000
574.0000000000 -780.0000000000 -2646.0000000000
574.0000000000 -780.0000000000 -2646.0000000000
574.0000000000 -780.0000000000 -2646.0000000000
444.0000000000 -484.0000000000 -2747.0000000000
444.0000000000 -484.0000000000 -2747.0000000000
444.0000000000 -484.0000000000 -2747.0000000000
142.0000000000 -280.0000000000 -2820.0000000000
142.0000000000 -280.0000000000 -2820.0000000000
142.0000000000 -280.0000000000 -2820.0000000000
177.0000000000 178.0000000000 -2850.0000000000
177.0000000000 178.0000000000 -2850.0000000000
177.0000000000 178.0000000000 -2850.0000000000
-19.0000000000 571.0000000000 -2824.0000000000
-19.0000000000 571.0000000000 -2824.0000000000
-19.0000000000 571.0000000000 -2824.0000000000
-571.0000000000 867.0000000000 -2725.0000000000
-571.0000000000 867.0000000000 -2725.0000000000
-571.0000000000 867.0000000000 -2725.0000000000
-1286.0000000000 1002.0000000000 -2463.0000000000
-1286.0000000000 1002.0000000000 -2463.0000000000
-1286.0000000000 1002.0000000000 -2463.0000000000
-1442.0000000000 872.0000000000 -2406.0000000000
-1442.0000000000 872.0000000000 -2406.0000000000
-1442.0000000000 872.0000000000 -2406.0000000000
-1323.0000000000 756.0000000000 -2493.0000000000
-1323.0000000000 756.0000000000 -2493.0000000000
-1323.0000000000 756.0000000000 -2493.0000000000
-1251.0000000000 634.0000000000 -2558.0000000000
-1251.0000000000 634.0000000000 -2558.0000000000
-1251.0000000000 634.0000000000 -2558.0000000000
-1251.0000000000 634.0000000000 -2814.0000000000
-1139.0000000000 579.0000000000 -2624.0000000000
-1139.0000000000 579.0000000000 -2624.0000000000
-1139.0000000000 579.0000000000 -2624.0000000000
-1072.0000000000 535.0000000000 -2653.0000000000
-1072.0000000000 535.0000000000 -2653.0000000000
-1072.0000000000 535.0000000000 -2653.0000000000

[jremington]

Here is what I get for your input data file, using my slightly edited version of magneto

Average magnitude (default Hm) and sigma of 2279 vectors = 2824.5, 134.6

Reject outliers? (0 or d, reject if > d*sigma from mean) 0
Rejection level selected: 0.0

2279 measurements processed, expected 2279

Expected norm of local field vector Hm? (Enter 0 for default 2824.5) 0

Set Hm = 2824.5

Combined bias vector B:
-34.49 67.93 161.43

Correction matrix Ainv, using Hm= 2824.5:
0.97249 0.01374 0.00522
0.01374 0.97557 0.02178
0.00522 0.02178 0.98110

Where Hcalc = Ainv*(H-B)

Code initialization statements...

float B[3]
{ -34.49, 67.93, 161.43};

float Ainv[3][3]
{{ 0.97249, 0.01374, 0.00522},
{ 0.01374, 0.97557, 0.02178},
{ 0.00522, 0.02178, 0.98110}};
output filename for corrected values (csv) mag0_corrected.csv

RMS corrected vector length 2824.535511

This is the corrected data file:

-1080.62, 1575.24,-2632.14
-1080.62, 1575.24,-2632.14
-1080.74, 1570.12,-2643.04
-1080.74, 1570.12,-2643.04
-1080.74, 1570.12,-2643.04
-1080.67, 1569.49,-2627.37
-1080.67, 1569.49,-2627.37
-1080.67, 1569.49,-2627.37
-1088.52, 1569.10,-2640.16
-1088.52, 1569.10,-2640.16
-1088.52, 1569.10,-2640.16
-1082.64, 1570.29,-2634.23
-1082.64, 1570.29,-2634.23
-1082.64, 1570.29,-2634.23
-1088.53, 1568.14,-2639.21
-1088.53, 1568.14,-2639.21
-1088.53, 1568.14,-2639.21
-1085.69, 1564.20,-2643.20
-1085.69, 1564.20,-2643.20
-1085.69, 1564.20,-2643.20
-1083.68, 1564.48,-2631.42
-1083.68, 1564.48,-2631.42
-1083.68, 1564.48,-2631.42
-1083.68, 1564.48,-2631.42
-1094.28, 1573.91,-2639.11
-1094.28, 1573.91,-2639.11
-1094.28, 1573.91,-2639.11
-1077.92, 1563.33,-2643.18
-1077.92, 1563.33,-2643.18
-1077.92, 1563.33,-2643.18
 -944.97, 1728.60,-2572.15
 -944.97, 1728.60,-2572.15
 -944.97, 1728.60,-2572.15
-1111.42, 1533.49,-2648.92
-1111.42, 1533.49,-2648.92
-1111.42, 1533.49,-2648.92
-1540.28,  939.23,-2754.53
-1540.28,  939.23,-2754.53
-1540.28,  939.23,-2754.53
-1558.63,  924.80,-2690.22
-1558.63,  924.80,-2690.22
-1558.63,  924.80,-2690.22
-1090.29,  914.16,-2718.49
-1090.29,  914.16,-2718.49
-1090.29,  914.16,-2718.49
 -724.12,  940.73,-2674.86
 -724.12,  940.73,-2674.86
 -724.12,  940.73,-2674.86
 -513.15,  986.99,-2611.97
 -513.15,  986.99,-2611.97
 -513.15,  986.99,-2611.97
 -402.35, 1158.37,-2515.42
 -402.35, 1158.37,-2515.42
 -402.35, 1158.37,-2515.42
 -534.54, 1491.41,-2272.35
 -534.54, 1491.41,-2272.35
 -534.54, 1491.41,-2272.35
 -850.51, 1569.77,-2099.62
 -850.51, 1569.77,-2099.62
 -850.51, 1569.77,-2099.62
-1226.41, 1533.30,-1923.87
-1226.41, 1533.30,-1923.87
-1226.41, 1533.30,-1923.87
-1226.41, 1533.30,-1923.87
-1566.88, 1402.82,-1789.25
-1566.88, 1402.82,-1789.25
-1566.88, 1402.82,-1789.25
-1872.57, 1239.74,-1608.11
-1872.57, 1239.74,-1608.11
-1872.57, 1239.74,-1608.11
-2080.18, 1084.82,-1472.38
-2080.18, 1084.82,-1472.38
-2080.18, 1084.82,-1472.38
-2216.77,  995.68,-1317.18
-2216.77,  995.68,-1317.18
-2216.77,  995.68,-1317.18
-2422.64,  738.53,-1132.73
-2422.64,  738.53,-1132.73
-2422.64,  738.53,-1132.73
-2624.04,  421.99, -859.36
-2624.04,  421.99, -859.36
-2624.04,  421.99, -859.36
-2750.02, -132.41, -588.95
-2750.02, -132.41, -588.95
-2750.02, -132.41, -588.95
-2703.13, -867.53,  -48.10
-2703.13, -867.53,  -48.10
-2703.13, -867.53,  -48.10
-2401.39,-1452.96,  487.57
-2401.39,-1452.96,  487.57
-2401.39,-1452.96,  487.57
-1959.38,-1723.24, 1166.28
-1959.38,-1723.24, 1166.28
-1959.38,-1723.24, 1166.28
-1425.69,-1880.00, 1586.16
-1425.69,-1880.00, 1586.16
-1425.69,-1880.00, 1586.16
-1287.21,-1683.94, 1900.11
-1287.21,-1683.94, 1900.11
-1287.21,-1683.94, 1900.11
-1287.21,-1683.94, 1900.11
-1311.10,-1436.78, 2070.23
-1311.10,-1436.78, 2070.23
-1311.10,-1436.78, 2070.23
-1211.38,-1184.69, 2252.85
-1211.38,-1184.69, 2252.85
-1211.38,-1184.69, 2252.85
-1030.97, -999.90, 2427.52
-1030.97, -999.90, 2427.52
-1030.97, -999.90, 2427.52
 -657.30, -953.78, 2556.93
 -657.30, -953.78, 2556.93
 -657.30, -953.78, 2556.93
 -368.59, -854.87, 2658.65
 -368.59, -854.87, 2658.65
 -368.59, -854.87, 2658.65
  -15.02, -628.83, 2737.05
  -15.02, -628.83, 2737.05
  -15.02, -628.83, 2737.05
  550.84, -481.98, 2714.74
  550.84, -481.98, 2714.74
  550.84, -481.98, 2714.74
  840.38, -334.08, 2650.85
  840.38, -334.08, 2650.85
  840.38, -334.08, 2650.85
 1298.94, -220.75, 2459.57
 1298.94, -220.75, 2459.57
 1298.94, -220.75, 2459.57
 1752.01, -317.86, 2145.91
 1752.01, -317.86, 2145.91
 1752.01, -317.86, 2145.91
 2038.84, -550.94, 1828.38
 2038.84, -550.94, 1828.38
 2038.84, -550.94, 1828.38
 2258.74, -693.94, 1483.10
 2258.74, -693.94, 1483.10
 2258.74, -693.94, 1483.10
 2258.74, -693.94, 1483.10
 2415.75, -861.60, 1076.16
 2415.75, -861.60, 1076.16
 2415.75, -861.60, 1076.16
 2580.31, -919.59,  509.90
 2580.31, -919.59,  509.90
 2580.31, -919.59,  509.90
 2614.45, -877.92, -359.77
 2614.45, -877.92, -359.77
 2614.45, -877.92, -359.77
 2453.84, -914.06, -878.15
 2453.84, -914.06, -878.15
 2453.84, -914.06, -878.15
 2345.49, -915.80,-1152.32
 2345.49, -915.80,-1152.32
 2345.49, -915.80,-1152.32
 2153.43, -902.55,-1489.34
 2153.43, -902.55,-1489.34
 2153.43, -902.55,-1489.34
 1991.63, -933.34,-1677.16
 1991.63, -933.34,-1677.16
 1991.63, -933.34,-1677.16
 1950.34, -869.32,-1753.41
 1950.34, -869.32,-1753.41
 1950.34, -869.32,-1753.41
 2030.18, -805.89,-1713.35
 2030.18, -805.89,-1713.35
 2030.18, -805.89,-1713.35
 2012.60, -729.85,-1746.07
 2012.60, -729.85,-1746.07
 2012.60, -729.85,-1746.07
 1863.84, -672.15,-1908.31
 1863.84, -672.15,-1908.31
 1863.84, -672.15,-1908.31
 1702.70, -439.24,-2118.69
 1702.70, -439.24,-2118.69
 1702.70, -439.24,-2118.69
 1150.85, -136.51,-2495.21
 1150.85, -136.51,-2495.21
 1150.85, -136.51,-2495.21
 1150.85, -136.51,-2495.21
  622.29,   88.28,-2663.50
  622.29,   88.28,-2663.50
  622.29,   88.28,-2663.50
  -32.63,  208.36,-2725.91
  -32.63,  208.36,-2725.91
  -32.63,  208.36,-2725.91
 -650.52,  283.34,-2646.96
 -650.52,  283.34,-2646.96
 -650.52,  283.34,-2646.96
 -953.96,  248.69,-2566.89
 -953.96,  248.69,-2566.89
 -953.96,  248.69,-2566.89
-1180.27,  233.80,-2480.11
-1180.27,  233.80,-2480.11
-1180.27,  233.80,-2480.11
-1518.06,  208.72,-2298.03
-1518.06,  208.72,-2298.03
-1518.06,  208.72,-2298.03
-1943.76,  108.21,-1988.63
-1943.76,  108.21,-1988.63
-1943.76,  108.21,-1988.63
-2152.86,   32.68,-1789.36
-2152.86,   32.68,-1789.36
-2152.86,   32.68,-1789.36
-2402.90, -103.54,-1442.61
-2402.90, -103.54,-1442.61
-2402.90, -103.54,-1442.61
-2597.03, -214.04,-1070.48
-2597.03, -214.04,-1070.48
-2597.03, -214.04,-1070.48
-2697.09, -376.98, -793.18
-2697.09, -376.98, -793.18
-2697.09, -376.98, -793.18
-2746.55, -399.69, -510.55
-2746.55, -399.69, -510.55
-2746.55, -399.69, -510.55
-2746.55, -399.69, -510.55
-2793.87, -469.30,  -57.34
-2793.87, -469.30,  -57.34
-2793.87, -469.30,  -57.34
-2784.73, -512.55,  140.80
-2784.73, -512.55,  140.80
-2784.73, -512.55,  140.80
-2727.01, -703.90,  356.49
-2727.01, -703.90,  356.49
-2727.01, -703.90,  356.49
-2627.48, -997.31,  471.07
-2627.48, -997.31,  471.07
-2627.48, -997.31,  471.07
-2556.54,-1129.13,  637.16
-2556.54,-1129.13,  637.16
-2556.54,-1129.13,  637.16
-2329.84,-1356.67,  916.63
-2329.84,-1356.67,  916.63
-2329.84,-1356.67,  916.63
-2112.38,-1381.07, 1306.48
-2112.38,-1381.07, 1306.48
-2112.38,-1381.07, 1306.48
-1781.37,-1251.24, 1809.18
-1781.37,-1251.24, 1809.18
-1781.37,-1251.24, 1809.18
-1408.96,-1164.30, 2154.24
-1408.96,-1164.30, 2154.24
-1408.96,-1164.30, 2154.24
 -839.27,-1535.36, 2206.72
 -839.27,-1535.36, 2206.72
 -839.27,-1535.36, 2206.72
 -263.54,-1563.88, 2315.88
 -263.54,-1563.88, 2315.88
 -263.54,-1563.88, 2315.88
  -13.03,-1805.90, 2146.04
  -13.03,-1805.90, 2146.04
  -13.03,-1805.90, 2146.04
  273.66,-1805.39, 2120.04
  273.66,-1805.39, 2120.04
  273.66,-1805.39, 2120.04
  273.66,-1805.39, 2120.04
  616.38,-1762.16, 2094.30
  616.38,-1762.16, 2094.30
  616.38,-1762.16, 2094.30
 1331.58,-1887.13, 1589.14
 1331.58,-1887.13, 1589.14
 1331.58,-1887.13, 1589.14
 1797.54,-1839.38, 1116.97
 1797.54,-1839.38, 1116.97
 1797.54,-1839.38, 1116.97
 2117.31,-1802.74,  411.42
 2117.31,-1802.74,  411.42
 2117.31,-1802.74,  411.42
 2161.15,-1789.70,    4.00
 2161.15,-1789.70,    4.00
 2161.15,-1789.70,    4.00
 2075.50,-1871.83, -172.80
 2075.50,-1871.83, -172.80
 2075.50,-1871.83, -172.80
 1813.68,-2121.26, -309.12
 1813.68,-2121.26, -309.12
 1813.68,-2121.26, -309.12
 1896.85,-2079.98, -126.37
 1896.85,-2079.98, -126.37
 1896.85,-2079.98, -126.37
 2054.12,-1923.49,  -26.97
 2054.12,-1923.49,  -26.97
 2054.12,-1923.49,  -26.97
 2214.70,-1658.67, -540.96
 2214.70,-1658.67, -540.96
 2214.70,-1658.67, -540.96
 2364.85,-1328.77, -794.68
 2364.85,-1328.77, -794.68
 2364.85,-1328.77, -794.68
 2398.43,-1159.56, -967.25
 2398.43,-1159.56, -967.25
 2398.43,-1159.56, -967.25
 2398.43,-1159.56, -967.25
 2399.85, -902.59,-1206.67
 2399.85, -902.59,-1206.67
 2399.85, -902.59,-1206.67
 2398.88, -641.64,-1352.86
 2398.88, -641.64,-1352.86
 2398.88, -641.64,-1352.86
 2368.77, -704.71,-1364.22
 2368.77, -704.71,-1364.22
 2368.77, -704.71,-1364.22
 2357.55, -814.87,-1356.92
 2357.55, -814.87,-1356.92
 2357.55, -814.87,-1356.92
 2377.35, -882.34,-1290.66
 2377.35, -882.34,-1290.66
 2377.35, -882.34,-1290.66
 2335.83, -903.20,-1369.78
 2335.83, -903.20,-1369.78
 2335.83, -903.20,-1369.78
 2350.33, -840.55,-1367.34
 2350.33, -840.55,-1367.34
 2350.33, -840.55,-1367.34
 2350.58, -564.75,-1504.36
 2350.58, -564.75,-1504.36
 2350.58, -564.75,-1504.36
 2152.66, -421.43,-1818.90
 2152.66, -421.43,-1818.90
 2152.66, -421.43,-1818.90
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-1257.76, 1645.59,-1896.03
-1257.76, 1645.59,-1896.03
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-1071.50, 1493.69,-2137.73
-1071.50, 1493.69,-2137.73
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 -831.30, 1582.85,-2181.60
 -831.30, 1582.85,-2181.60
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 -380.51, 1971.03,-1974.57
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   91.01, 2232.43,-1738.87
   91.01, 2232.43,-1738.87
   91.01, 2232.43,-1738.87
  649.99, 2343.42,-1482.54
  649.99, 2343.42,-1482.54
  649.99, 2343.42,-1482.54
 1031.47, 2273.34,-1410.59
 1031.47, 2273.34,-1410.59
 1031.47, 2273.34,-1410.59
 1558.80, 2053.86,-1309.85
 1558.80, 2053.86,-1309.85
 1558.80, 2053.86,-1309.85
 1744.08, 1937.59,-1262.47
 1744.08, 1937.59,-1262.47
 1744.08, 1937.59,-1262.47
 2137.15, 1797.89, -819.39
 2137.15, 1797.89, -819.39
 2137.15, 1797.89, -819.39
 2479.61, 1361.25, -722.46
 2479.61, 1361.25, -722.46
 2479.61, 1361.25, -722.46
 2762.10,  621.21, -685.53
 2762.10,  621.21, -685.53
 2762.10,  621.21, -685.53
 2774.30,   11.98, -821.60
 2774.30,   11.98, -821.60
 2774.30,   11.98, -821.60
 2766.68, -529.44, -832.71
 2703.47, -530.33, -833.05
 2703.47, -530.33, -833.05
 2703.47, -530.33, -833.05
 2463.86,-1221.81, -737.86
 2463.86,-1221.81, -737.86
 2463.86,-1221.81, -737.86
 1981.05,-1887.64, -739.43
 1981.05,-1887.64, -739.43
 1981.05,-1887.64, -739.43
 1638.75,-2149.15, -797.97
 1638.75,-2149.15, -797.97
 1638.75,-2149.15, -797.97
 1257.29,-2356.88, -868.26
 1257.29,-2356.88, -868.26
 1257.29,-2356.88, -868.26
  942.27,-2510.54, -828.17
  942.27,-2510.54, -828.17
  942.27,-2510.54, -828.17
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  681.22,-2597.16, -876.52
  681.22,-2597.16, -876.52
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  501.95,-2613.02, -907.20
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  -79.43,-2626.40,-1055.56
  -79.43,-2626.40,-1055.56
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-1542.89,-1422.30,-1930.45
-1542.89,-1422.30,-1930.45
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-1827.89, -857.96,-1976.21
-1827.89, -857.96,-1976.21
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-1791.49, -571.80,-2107.92
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-1679.98,  718.27,-2077.66
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-1226.29, 1594.79,-1876.41
-1226.29, 1594.79,-1876.41
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    0.79, 2395.14,-1377.79
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  659.50, 2472.71,-1108.95
  659.50, 2472.71,-1108.95
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  984.71, 2332.77,-1206.52
  984.71, 2332.77,-1206.52
  984.71, 2332.77,-1206.52
 1319.33, 2011.47,-1443.40
 1319.33, 2011.47,-1443.40
 1319.33, 2011.47,-1443.40
 1602.38, 1561.89,-1717.72
 1602.38, 1561.89,-1717.72
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 2102.02,  419.43,-1852.41
 2102.02,  419.43,-1852.41
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 1019.51,  193.47,-2655.23
 1019.51,  193.47,-2655.23
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 1027.40,  264.18,-2636.95
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-1150.99, 1246.96,-2178.93
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   80.63,-1297.27,-2506.84
   80.63,-1297.27,-2506.84
   80.63,-1297.27,-2506.84
  229.26,-1222.02,-2548.54
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  346.34,-1189.39,-2558.01
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 1096.86,-1321.57,-2398.31
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 1242.95,-1424.56,-2254.73
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 1253.86,-1605.17,-2096.90
 1253.86,-1605.17,-2096.90
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 1339.09,-1553.60,-2111.01
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 1424.69,-1306.03,-2211.95
 1424.69,-1306.03,-2211.95
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 1355.56, -957.88,-2427.15
 1355.56, -957.88,-2427.15
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 1235.25, -909.73,-2510.03
 1235.25, -909.73,-2510.03
 1235.25, -909.73,-2510.03
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  954.73,-1054.95,-2595.09
  954.73,-1054.95,-2595.09
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  442.56, -595.23,-2862.99
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  151.29, -401.95,-2931.75
  151.29, -401.95,-2931.75
  191.46,   44.69,-2951.02
  191.46,   44.69,-2951.02
  191.46,   44.69,-2951.02
    6.39,  425.96,-2917.97
    6.39,  425.96,-2917.97
    6.39,  425.96,-2917.97
 -525.84,  709.30,-2817.28
 -525.84,  709.30,-2817.28
 -525.84,  709.30,-2817.28
-1217.94,  836.89,-2561.02
-1217.94,  836.89,-2561.02
-1217.94,  836.89,-2561.02
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-1371.14,  709.16,-2508.74
-1371.14,  709.16,-2508.74
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-1257.46,  595.74,-2596.00
-1257.46,  595.74,-2596.00
-1189.46,  476.29,-2662.06
-1189.46,  476.29,-2662.06
-1189.46,  476.29,-2662.06
-1190.79,  470.71,-2913.22
-1081.64,  422.73,-2727.42
-1081.64,  422.73,-2727.42
-1081.64,  422.73,-2727.42
-1017.24,  380.10,-2756.48
-1017.24,  380.10,-2756.48
-1017.24,  380.10,-2756.48

Rerun magneto with the above file to get

Average magnitude (default Hm) and sigma of 2279 vectors = 2823.5, 76.1

Reject outliers? (0 or d, reject if > d*sigma from mean) 0
Rejection level selected: 0.0

2279 measurements processed, expected 2279

Expected norm of local field vector Hm? (Enter 0 for default 2823.5) 0

Set Hm = 2823.5

Combined bias vector B:
-0.16 -0.67 -0.15

Correction matrix Ainv, using Hm= 2823.5:
0.99962 0.00024 0.00010
0.00024 0.99973 0.00031
0.00010 0.00031 0.99973

Where Hcalc = Ainv*(H-B)

Code initialization statements...

float B[3]
{ -0.16, -0.67, -0.15};

float Ainv[3][3]
{{ 0.99962, 0.00024, 0.00010},
{ 0.00024, 0.99973, 0.00031},
{ 0.00010, 0.00031, 0.99973}};

[PYJTER]

I tried your script and for
49668.3 nT

Latitude:	50.1551° N
Longitude:	19.0125° E
Elevation:	311.0 m Mean Sea Level

From this site:
https://www.ngdc.noaa.gov/geomag/calculators/magcalc.shtml#igrfwmm

I used the same raw data and generate output file and again import to magneto:

Rejection level selected: 0.0

2279 measurements processed, expected 2279

Expected norm of local field vector Hm? (Enter 0 for default 49650.3) 49668.3

Set Hm = 49668.3

Combined bias vector B:
-2.89 -11.79 -2.67

Correction matrix Ainv, using Hm= 49668.3:
0.99998 0.00024 0.00010
0.00024 1.00009 0.00031
0.00010 0.00031 1.00009

Where Hcalc = Ainv*(H-B)

Code initialization statements...

float B[3]
{ -2.89, -11.79, -2.67};

float Ainv[3][3]
{{ 0.99998, 0.00024, 0.00010},
{ 0.00024, 1.00009, 0.00031},
{ 0.00010, 0.00031, 1.00009}};

I do not know if the problem is not the magnetic field value. As if he could not calculate correctly. I can't get values close to 0 in Combined bias vector B.
Unless these values are ok, but they aren't close to 0.

[jremington]

For a compass, the value of Hm is completely arbitrary, and there is no need to use such a large number. The raw input data values are scaled accordingly, which can cause problems with numerical precision.

I choose a number for Hm such that the diagonal matrix elements are about 1, and a convenient choice is the average magnitude of the input data vectors.

For the compass calculation, the corrected magnetic field vector is normalized so that its value is always 1.000. Only the direction of the vector matters.

You need only a few hundred points, but they should more evenly distributed over the angular space (unit sphere). Here is what those corrected data points look like:

[PYJTER]

And now everything is clear. So I am looking for a value that would be as close to 1 as possible in the diagonal matrix. And for the accelerometer I set the constant 1000.

1. What program do you generate a spherical chart in?

2. This is probably the last straight ahead of me now. Tilt compensation. I don't know what the constant alpha angle means and where it came from and on what basis it was calculated.
   Is tilt compensated ok?

const float alpha = 0.15;


// Accelerometer calibration
Xa_off = compass.a.x - 8191.1;
Ya_off = compass.a.y - 9432.26;
Za_off = compass.a.z - 1071.55;
Xa_cal =  0.05896*Xa_off + 0.00394*Ya_off - 0.00277*Za_off;
Ya_cal =  0.00394*Xa_off + 0.05488*Ya_off + 0.00491*Za_off;
Za_cal = -0.00277*Xa_off + 0.00491*Ya_off + 0.03929*Za_off;

// Magnetometer calibration
Xm_off = compass.m.x* + 34.49;
Ym_off = compass.m.y* - 67.93;
Zm_off = compass.m.z* - 161.43;
Xm_cal =  0.97249*Xm_off + 0.01374*Ym_off + 0.00522*Zm_off;
Ym_cal =  0.01374*Xm_off +  0.97557*Ym_off +  0.02178*Zm_off;
Zm_cal =  0.00522*Xm_off + 0.02178*Ym_off + 0.98110*Zm_off;

// Low-Pass filter accelerometer
fXa = Xa_cal * alpha + (fXa * (1.0 - alpha));
fYa = Ya_cal * alpha + (fYa * (1.0 - alpha));
fZa = Za_cal * alpha + (fZa * (1.0 - alpha));

// Low-Pass filter magnetometer
fXm = Xm_cal * alpha + (fXm * (1.0 - alpha));
fYm = Ym_cal * alpha + (fYm * (1.0 - alpha));
fZm = Zm_cal * alpha + (fZm * (1.0 - alpha));

// Pitch and roll
roll  = atan2(fYa, sqrt(fXa*fXa + fZa*fZa));
pitch = atan2(fXa, sqrt(fYa*fYa + fZa*fZa));
roll_print = roll*180.0/M_PI;
pitch_print = pitch*180.0/M_PI;

// Tilt compensated magnetic sensor measurements
fXm_comp = fXm*cos(pitch)+fZm*sin(pitch);
fYm_comp = fXm*sin(roll)*sin(pitch)+fYm*cos(roll)-fZm*sin(roll)*cos(pitch);

// Arctangent of y/x
Heading = (atan2(fYm_comp,fXm_comp)*180.0)/M_PI;
if (Heading < 0)
Heading += 360;

EDIT:
Something is wrong, because if I put the sensor horizontally to the ground and rotate it around its axis, it should show me a 0-360 degree range. A shows me 135 degrees to 160 degrees. If I put it vertically, it shows 0 degrees.
Perhaps this compensation is not for the boat because I only want him to compensate for the tilt.

Heading: 135.50
Heading: 135.92
Heading: 136.59
Heading: 136.08
Heading: 136.88
Heading: 137.27
Heading: 139.00
Heading: 139.97
Heading: 141.68
Heading: 142.34
Heading: 143.44
Heading: 145.59
Heading: 146.66
Heading: 147.40
Heading: 147.96
Heading: 148.29
Heading: 148.50
Heading: 149.73
Heading: 150.00
Heading: 150.80
Heading: 151.73
Heading: 152.15
Heading: 153.60
Heading: 153.59
Heading: 153.96
Heading: 154.01
Heading: 154.48
Heading: 156.25
Heading: 156.60
Heading: 156.29
Heading: 156.42
Heading: 155.82
Heading: 155.29
Heading: 155.18
Heading: 155.02
Heading: 154.70
Heading: 154.30
Heading: 153.92
Heading: 153.42
Heading: 152.34
Heading: 152.38
Heading: 151.48
Heading: 149.94
Heading: 148.39
Heading: 147.21
Heading: 146.07
Heading: 144.87
Heading: 143.82
Heading: 142.95
Heading: 140.96
Heading: 139.67
Heading: 137.87
Heading: 137.29
Heading: 135.89
Heading: 134.14
Heading: 133.38
Heading: 129.39
Heading: 129.20
Heading: 130.47
Heading: 130.98
Heading: 132.56
Heading: 132.53
Heading: 132.71
Heading: 133.04
Heading: 132.83
Heading: 133.43
Heading: 134.18
Heading: 135.51
Heading: 136.23
Heading: 136.31
Heading: 137.31
Heading: 138.12
Heading: 139.20
Heading: 139.42
Heading: 139.94
Heading: 140.65
Heading: 141.54
Heading: 142.60
Heading: 143.48
Heading: 144.11
Heading: 144.62
Heading: 145.72
Heading: 146.69
Heading: 147.45
Heading: 148.80
Heading: 149.95
Heading: 150.81
Heading: 151.68
Heading: 152.77
Heading: 153.72
Heading: 154.93
Heading: 155.74
Heading: 156.28
Heading: 156.57
Heading: 157.29
Heading: 157.79
Heading: 157.98
Heading: 158.68
Heading: 159.17
Heading: 159.34
Heading: 159.63
Heading: 159.92
Heading: 160.73
Heading: 160.62
Heading: 160.74
Heading: 160.03
Heading: 161.40
Heading: 161.99
Heading: 160.68
Heading: 160.97
Heading: 160.59
Heading: 160.49
Heading: 160.40
Heading: 160.34
Heading: 159.65
Heading: 159.83
Heading: 156.51
Heading: 155.97
Heading: 153.34
Heading: 153.35
Heading: 151.85
Heading: 150.65
Heading: 149.57
Heading: 147.85
Heading: 146.05
Heading: 145.05
Heading: 143.50
Heading: 142.29
Heading: 140.78
Heading: 139.91
Heading: 138.50
Heading: 137.84
Heading: 136.40
Heading: 135.39
Heading: 133.87
Heading: 133.45
Heading: 135.01
Heading: 134.54
Heading: 134.92
Heading: 135.30
Heading: 134.72
Heading: 134.20
Heading: 134.02
Heading: 134.95
Heading: 134.83
Heading: 136.18
Heading: 136.15
Heading: 136.25
Heading: 135.84
Heading: 135.42
Heading: 135.57
Heading: 135.75
Heading: 136.39
Heading: 136.85
Heading: 137.26
Heading: 137.76
Heading: 138.49
Heading: 138.81
Heading: 139.25
Heading: 139.63
Heading: 140.01
Heading: 140.59
Heading: 140.97
Heading: 141.68
Heading: 142.32
Heading: 142.78
Heading: 143.31
Heading: 143.85
Heading: 144.47
Heading: 145.25
Heading: 145.78
Heading: 146.40
Heading: 146.88
Heading: 147.14
Heading: 147.55
Heading: 148.25
Heading: 148.76
Heading: 149.47
Heading: 149.97
Heading: 150.30
Heading: 150.84
Heading: 151.37
Heading: 152.02
Heading: 152.61
Heading: 153.11
Heading: 153.38
Heading: 153.69
Heading: 154.46
Heading: 154.49
Heading: 154.49
Heading: 154.93
Heading: 154.96
Heading: 153.44
Heading: 154.71

EDIT2
I applied the script from here:

pololu/lsm303-arduino/blob/master/examples/Heading/Heading.ino

With the reference vector and it looks ok. Nothing tomorrow I will go to the water and test.
And the calibration will be useful for mpu9250 only when I try to compile I have an error

Compile error min() required

[PYJTER]

Something went wrong with this lsm303d. I swam today but the waves were too big to say anything. I thought the boat was going straight to the destination the first time but then it kind of went crazy. Target point was about 50m on the right

20220108_135026.mp4

She began to spin around herself. If it is calmer without wind, I will do longer tests. Unfortunately, winter in Poland; (
Yesterday I received the mpu9250 module and I would like to base my struggles with my boat on it.
@jremington I used your script to get calibration data:

MPU-9250-AHRS/MPU-9250_cal at master · jremington/MPU-9250-AHRS

I have received the following data

Done. Gyro offsets (raw) 75.4, -170.7, 175.9
Collecting 300 points for scaling, 3/second
TURN SENSOR VERY SLOWLY AND CAREFULLY IN 3D
-32768 -18104 12544 -8 43 23
-32768 -18036 12464 -7 43 24
-32768 -18080 12412 -8 42 21
-28436 -18336 11096 -7 42 19
-32768 -18024 12720 -8 49 20
-32768 -18444 12012 -7 54 19
-32768 -18740 14184 -7 60 18
-32768 -17492 12036 -9 58 16
-32768 -18556 13780 -6 50 15
-32768 -18680 13032 -10 41 16
-32768 -18796 13416 -11 41 14
-32768 -18544 12924 -12 37 9
-32768 -18620 12796 -14 39 11
-32768 -18548 13028 -15 35 11
-32768 -17984 12536 -18 34 11
-32768 -16916 13812 -23 33 12
-32768 -17780 14568 -20 34 14
-32768 -17108 12860 -20 35 15
-32768 -19208 11960 -18 34 17
-32768 -18620 12840 -21 37 19
-32768 -18264 13456 -15 41 22
-32768 -17372 13492 -17 38 22
-32768 -15988 14648 -17 37 27
-32768 -17616 12952 -15 37 29
-32768 -18288 13756 -18 33 34
-32768 -18740 14664 -19 40 36
-32768 -17796 13660 -20 38 39
-32768 -16940 12284 -23 39 40
-32768 -17592 14420 -21 42 42
-32768 -18048 13052 -18 48 41
-32768 -18116 12824 -11 49 40
-32768 -20900 13584 -6 47 39
-32768 -17992 13856 -11 35 38
-32768 -19204 13008 -16 34 37
-32768 -17068 14372 -16 33 35
-32768 -19168 16000 -15 31 32
-32768 -20108 13888 -11 23 28
-32768 -24428 12408 -1 18 20
-32284 -25620 8640 7 10 13
-32228 -28224 7080 15 10 2
-32768 -31804 5632 22 8 -7
-32744 -32436 -1988 24 9 -19
-32768 -32768 -36 26 9 -23
-32768 -32768 -5780 27 12 -32
-32768 -32768 -10612 28 15 -46
-32768 -30736 -14804 24 17 -59
-32768 -26460 -17152 16 20 -72
-32768 -21956 -18988 -2 15 -78
-32616 -18700 -18940 -7 18 -84
-32768 -19260 -19780 -16 16 -84
-32768 -13308 -21632 -33 19 -84
-30368 -6248 -15236 -56 15 -73
-32768 -4992 -14488 -69 15 -59
-32768 -2476 -4896 -79 17 -42
-32768 -1588 -1628 -81 23 -23
-32768 -3208 5616 -76 25 -1
-32768 -7208 11872 -53 14 14
-32768 -17092 9212 -19 12 25
-32768 -23564 14188 11 16 11
-32768 -31708 3680 28 21 -9
-32768 -32768 -4612 34 29 -28
-32768 -32768 -9864 34 28 -49
-32768 -29900 -16372 29 36 -62
-32768 -27504 -16100 23 38 -73
-32768 -23376 -17820 16 39 -80
-32768 -19732 -18412 9 39 -85
-32768 -16516 -21404 -14 47 -96
-32768 -14908 -18800 -12 48 -95
-32768 -26656 -13776 23 31 -70
-32768 -32648 -12628 39 30 -44
-32768 -32768 -4304 37 29 -15
-32768 -31208 2716 17 25 9
-32768 -25964 9560 -11 26 22
-32608 -20228 8540 -29 14 21
-32768 -13112 10576 -52 18 11
-32768 -8900 9512 -62 19 -1
-32068 -4632 2724 -71 25 -13
-32768 -2560 -1556 -79 37 -23
-32768 -1120 -6740 -78 42 -50
-32768 -4280 -13380 -69 47 -75
-32768 -6960 -17096 -56 49 -81
-32768 -10160 -16816 -44 55 -92
-32768 -13616 -19360 -28 55 -94
-32768 -19180 -21284 -5 56 -91
-32768 -24124 -19300 13 65 -78
-32768 -28608 -15204 20 71 -65
-32768 -30720 -11416 28 68 -57
-32768 -32768 -13936 32 67 -39
-32768 -32768 -3216 34 60 -13
-32768 -32060 4280 18 52 12
-32768 -26904 8716 -1 47 21
-32768 -21184 11296 -29 37 22
-32768 -13664 13804 -46 33 16
-32768 -8852 9508 -61 29 3
-32768 -7768 10412 -63 27 -1
-31096 -4340 3984 -70 27 -6
-32464 -2652 6552 -75 27 -17
-32288 -4596 1440 -78 33 -20
-32768 -1260 -8348 -82 30 -40
-32768 -2616 -13144 -72 36 -71
-32768 -7052 -21376 -52 53 -92
-32768 -10300 -18640 -38 68 -98
-32768 -15144 -17240 -18 92 -86
-32768 -18904 -14296 3 87 -81
-32768 -25892 -11660 19 85 -63
-32768 -28072 -5712 29 77 -43
-32768 -31268 -1388 35 71 -35
-32768 -30184 -720 35 71 -28
-32768 -31296 2296 37 51 -17
-32768 -31368 5296 33 39 -3
-32768 -26120 10716 14 21 11
-32768 -22396 11704 -9 9 18
-32768 -13504 16816 -29 6 21
-32768 -11244 14132 -45 9 16
-32768 -8092 11696 -61 28 15
-32768 -4252 5436 -73 35 8
-32768 -1948 -1368 -84 55 -21
-32768 -4152 -9892 -82 64 -47
-32768 -7004 -13900 -71 66 -66
-32768 -9944 -17632 -51 57 -89
-32768 -18108 -20092 -30 52 -92
-32768 -22496 -18468 -12 49 -91
-32768 -28460 -14748 14 42 -81
-32768 -31612 -17672 21 36 -73
-32768 -31400 -11368 29 40 -67
-32768 -32768 -8788 28 37 -64
-32768 -32768 -9492 32 36 -56
-32768 -32768 -5964 32 24 -49
-32768 -32768 -2488 31 15 -38
-32768 -32768 1956 29 13 -20
-32768 -31304 6424 19 13 -1
-32768 -27108 9924 14 12 7
-32768 -23216 11704 -2 20 21
-32768 -20564 12756 -16 20 25
-32768 -17552 11684 -26 37 27
-32768 -16792 11480 -28 54 26
-32768 -17576 9808 -23 65 23
-32768 -17704 6512 -25 80 13
-32768 -19660 2544 -17 82 10
-32768 -20816 316 -18 98 -6
-32768 -20444 600 -14 100 -16
-32768 -19984 -10192 -16 103 -23
-32768 -20480 -9188 -17 103 -45
-32768 -19564 -17148 -17 94 -69
-32768 -17500 -20508 -21 68 -88
-32768 -17100 -21372 -28 40 -91
-29360 -15588 -15160 -29 14 -81
-23572 -16940 -10836 -25 1 -72
-25468 -21300 -8460 -20 -7 -56
-24360 -18460 712 -27 -15 -33
-27056 -17464 7012 -27 -16 -10
-32448 -18088 10340 -24 -7 9
-32340 -17208 13428 -20 3 17
-32768 -19464 12996 -22 13 22
-32768 -18996 11932 -21 39 24
-32768 -16872 8968 -29 67 19
-32768 -15568 2732 -30 90 2
-32768 -15740 -8552 -34 103 -36
-32768 -15800 -12844 -34 101 -48
-32768 -15752 268 -33 95 -6
-32768 -15584 11280 -31 58 24
-32768 -17812 14588 -25 14 22
-27540 -19468 13108 -15 -7 6
-31388 -26496 5368 -4 -8 -10
-23472 -23688 -508 -8 -14 -35
-22624 -25864 -8776 -6 -1 -61
-26964 -26104 -11812 -2 9 -72
-27788 -25720 -14332 -1 18 -80
-29120 -27360 -15684 2 25 -81
-32768 -27544 -17568 3 39 -82
-32768 -26044 -15136 2 64 -80
-32768 -25212 -14332 -6 91 -65
-32768 -23232 -7392 -15 100 -35
-32768 -19340 -4852 -19 94 -7
-32768 -21492 6396 -18 78 10
-32768 -19872 10848 -24 55 22
-32768 -22196 8880 -21 38 25
-32768 -21272 11352 -17 29 25
-32768 -20336 10820 -19 19 23
-32768 -23300 9796 -10 0 8
-27000 -24192 4148 -11 -12 -10
-23564 -22540 1724 -10 -14 -31
-24676 -21700 -2584 -11 -13 -46
-23372 -24176 -6708 -9 -4 -59
-26244 -22688 -13852 -13 12 -76
-30288 -23312 -16484 -10 23 -82
-32768 -21288 -22788 -15 60 -87
-32768 -20248 -20512 -18 74 -84
-32768 -19456 -15488 -23 95 -66
-32768 -21488 -7800 -19 102 -34
-32768 -23572 784 -7 82 5
-32768 -25924 4564 -5 59 19
-32768 -24604 11060 -10 41 27
-32768 -27884 8916 2 28 19
-32768 -30412 3936 11 6 6
-31664 -31880 484 11 -4 -18
-24144 -28572 2088 7 -6 -31
-30216 -29264 -4844 2 -3 -57
-25468 -25784 -10528 -7 2 -66
-27600 -24220 -10652 -10 7 -73
-29980 -25740 -22300 -10 34 -86
-32768 -24804 -18032 -11 60 -86
-32768 -22504 -16896 -14 84 -75
-32768 -21068 -11776 -19 101 -54
-32768 -20588 -3424 -21 101 -18
-32768 -21576 6328 -20 78 13
-32768 -20660 12244 -19 48 26
-32768 -21432 12772 -16 31 25
-32768 -22880 11504 -17 22 25
-32768 -22072 10692 -15 5 16
-30744 -22288 10140 -13 -6 3
-28176 -22784 8048 -9 -10 -9
-25276 -22168 4816 -10 -11 -14
-25176 -22188 1832 -14 -14 -34
-22780 -21784 -4340 -17 -11 -47
-24384 -21232 -4972 -19 -8 -54
-22880 -20016 -10020 -19 -3 -65
-25956 -18988 -14848 -23 15 -78
-30532 -19112 -15444 -23 30 -87
-32768 -19628 -18424 -22 44 -88
-32768 -20256 -19228 -17 62 -88
-32768 -20824 -19048 -16 82 -81
-32768 -22012 -15236 -13 96 -63
-32768 -22292 -7360 -11 103 -37
-32768 -23228 -3068 -7 96 -11
-32768 -24596 748 -8 84 7
-32768 -22452 14544 -13 34 26
-32768 -24916 11324 -12 7 15
-30212 -27240 6920 -4 -6 -1
-27356 -28616 -1848 1 -8 -14
-26852 -28452 -6696 3 -10 -31
-27580 -27048 -12572 1 -6 -55
-31260 -24976 -15652 -8 2 -71
-31096 -21980 -18680 -17 10 -76
-32768 -18664 -19328 -27 18 -82
-32768 -16204 -18280 -37 27 -86
-32768 -12940 -19072 -48 40 -85
-32768 -10068 -18272 -58 49 -81
-32768 -7020 -15556 -66 51 -72
-32768 -5460 -13008 -72 56 -64
-32768 -5024 -10708 -76 63 -51
-32768 -4508 -3024 -78 64 -37
-32768 -4440 -552 -80 60 -23
-32768 -4360 2148 -76 58 -13
-32768 -5164 3684 -75 56 -9
-32768 -6380 6336 -69 51 1
-32768 -7468 9208 -64 50 7
-32768 -10264 10224 -58 53 12
-32768 -11316 11128 -53 39 17
-32768 -17376 12164 -40 32 23
-32768 -18868 14232 -31 31 24
-32768 -24616 8656 -13 26 24
-32768 -25636 11872 -6 23 24
-32768 -28676 7000 3 23 18
-32768 -29052 9908 4 22 17
-32768 -29820 7668 7 20 13
-32768 -29776 4780 10 8 6
-30948 -30288 3332 5 2 2
-28900 -28824 2672 5 0 1
-28560 -29820 1904 4 -1 1
-31888 -31004 3924 11 3 -1
-31460 -31752 644 16 5 -7
-32480 -32200 1284 19 7 -10
-32484 -32768 -1348 21 5 -14
-32768 -32768 -3192 25 6 -22
-32768 -32712 -5996 24 8 -30
-32768 -31952 -8940 25 4 -40
-31820 -29892 -12512 19 4 -49
-30948 -27476 -13644 12 -1 -54
-29236 -23556 -15856 2 -4 -61
-27632 -20756 -14580 -6 -6 -61
-27024 -17280 -14824 -18 -9 -61
-27188 -14080 -13964 -25 -10 -57
-26136 -11836 -13072 -34 -11 -55
-26488 -8612 -11068 -45 -11 -49
-25868 -8240 -9016 -46 -14 -36
-26140 -8160 -7592 -46 -15 -36
-24660 -7496 -5952 -44 -15 -37
-21328 -8716 -1284 -39 -21 -30
-28328 -9892 -432 -35 -19 -27
-25192 -12084 -2004 -26 -21 -22
-24056 -14496 380 -19 -21 -17
-23456 -15364 404 -16 -20 -21
-23952 -15588 -752 -14 -18 -22
-23424 -14668 -172 -18 -19 -26
-23112 -13900 -2080 -23 -18 -33
-25184 -13356 -868 -24 -19 -31
-23656 -13984 180 -26 -19 -27
-23928 -15160 2328 -25 -17 -20
-22448 -15540 3328 -29 -15 -22
-23760 -17136 1376 -28 -15 -22
-24252 -18076 5868 -29 -15 -15
-24432 -17768 5860 -36 -12 -9
-27980 -17192 7568 -34 -7 0
-28416 -17972 8528 -36 -4 7
-31504 -16864 11512 -38 4 13
-32768 -13980 12672 -50 21 17
-32768 -15156 12388 -47 38 21
-32768 -15568 11524 -46 53 18
-32768 -13908 10008 -52 70 8
-32768 -9744 5260 -65 78 -9
Done. rms Acc = 39774.53, rms Mag = 71.72

And as always, it may be trivial for some people, but I don't understand it here.
As a result, we get the first 3 columns one after the other is the accelerometer and the next 3 are the magnetometer? But I have the data from the magnetometer in the form of two numbers, in tens and before that I had in the thousands. Is it correct ??

Should I separate these results separately for the accelerometer and separately for the magnetometer? And then correct the data in the magneto ??

Edit
Link to film

[jremington]

@PYJTER

Is it correct ??

Yes, the magnetometer and accelerometer scales are different.

Should I separate these results separately for the accelerometer and separately for the magnetometer?

Yes, Magneto calibrates each sensor separately.

Pololu has tilt-compensated compass code for the LSM303 Pololu - LSM303D 3D Compass and Accelerometer Carrier with Voltage Regulator

The code I wrote for the MPU-9250 uses the same algorithm.

[PYJTER]

Thank you for your patience @jremington.
Maybe I will not discuss mpu9250 for now and I will focus as the topic points to lsm303d.

I calibrated in accordance with Pololu recommendations and got the maximum and minimum values.
My code looks like this now. I checked with GPS, I get the correct angle readings in relation to the north and the compass also correctly indicates the north, i.e. 0.
For slight tilts to the right and left, about 30 degrees, the number of degrees does not change in the compass
So I think everything is fine up to this point.
Then i have implemented your error calculation algorithm and Kp. which then translates into the speed that is given to motors A or B. I have an H bridge so I swim forward all the time, only the motors decide whether I turn or not.
Could you please check if my code is ok ...?
I have cut the flow in MANUAL to make it more clean.

Thanks again in advance for your help.

void setup()
{
  Serial.begin(115200);
  ss.begin(GPSBaud);
  Serial2.begin(115200);
  Wire.begin();
  compass.init();
  compass.enableDefault();
  compass.m_min = (LSM303::vector<int16_t>){-2813, -2107, -2738};
  compass.m_max = (LSM303::vector<int16_t>){+2724, +2998, +3155};

  motorsSetup();
}


void loop()
{
  
  if(IBusServo.readChannel(7) > 1950)
  {
    Serial.println("AUTOPILOT");
    headingcal();
    delay(200);
    gpsdata();
    delay(200);
  }
 else
 {
    Serial.println("MANUAL");
    manualSter();
   }
  




 

}

// This custom version of delay() ensures that the gps object
// is being "fed".
static void smartDelay(unsigned long ms)
{
  unsigned long start = millis();
  do 
  {
    while (ss.available())
      gps.encode(ss.read());
  } while (millis() - start < ms);
}



void gpsdata()
{
  unsigned long start;
  double lat_val, lng_val, alt_m_val, speedkmph;
  bool loc_valid, alt_valid;
  lat_val = gps.location.lat(); 
  loc_valid = gps.location.isValid(); 
  lng_val = gps.location.lng();
  alt_m_val = gps.altitude.meters(); 
  alt_valid = gps.altitude.isValid(); 
  speedkmph= gps.speed.kmph();


   if (!loc_valid)
    {
      Serial.println("**NO FIX**");
      delay(100);
    }
    else
    {
    Serial.println("GPS READING: ");
    Serial.print("GPS FIX : ");
    Serial.println();
    latc=lat_val;
    longc=lng_val;

    //when is valid location run autopilot
    steering();
    }


  smartDelay(1000);

}

// convert degrees to radians
void radianConversion()
{
  deltaLatitudeRadians = (wayPointLatitude - latc) * pi / 180;
  deltaLongitudeRadians = (wayPointLongitude - longc)* pi / 180;
  latitudeRadians = latc * pi / 180;
  wayPointLatitudeRadians = wayPointLatitude * pi / 180;
  longitudeRadians = longc * pi / 180;
  wayPointLongitudeRadians = wayPointLongitude * pi / 180;
  
}

// calculate distance from present location to next way point
float calculateDistance()
{
  radianConversion();
  
  float a = sin(deltaLatitudeRadians/2) * sin(deltaLatitudeRadians/2) +
          sin(deltaLongitudeRadians/2) * sin(deltaLongitudeRadians/2) * 
          cos(latitudeRadians) * cos(wayPointLatitudeRadians);
  float c = 2 * atan2(sqrt(a), sqrt(1-a)); 
  float d = radiusOfEarth * c; // 
  return d * 0.621371192*1609.344; // distance 
}

// calculate bearing from present location to next way point
float calculateBearing()
{
  radianConversion();
  
  float y = sin(deltaLongitudeRadians) * cos(wayPointLatitudeRadians);
  float x = cos(latitudeRadians) * sin(wayPointLatitudeRadians) -
        sin(latitudeRadians) * cos(wayPointLatitudeRadians) * cos(deltaLongitudeRadians);
  
  bearing = atan2(y, x) / pi * 180;
  
  if(bearing < 0)
  {
    bearing = 360 + bearing;
  }
  
  return bearing;
}



//LSM303D run with tilt compensation
void headingcal()
{

  compass.read();
  
  int headingCompass = compass.heading((LSM303::vector<int>){0, -1, 0});
  Serial.print("heading: ");
  //Serial.print(mDirection);
  Serial.println(headingCompass);  
}


int motorSpeed=120, kp=40, bias=0, error=0;

//Motor:-
void steering()
{
  distanceToWaypoint = calculateDistance();
  bearing = calculateBearing();


// heading error and PID steering from jremington script
   error = heading_error(bearing, headingCompass);

//error is positive if current_heading > bearing (compass direction)
//positive bias acts to reduce left motor speed, so bear left
    bias = (kp*error)/10;  //Kp in tenths (Ki, Kd not necessary in water)



 Serial.println(distanceToWaypoint); 
  if(distanceToWaypoint > 3)
  {
        // Set Motor A forward
 
    digitalWrite(in1, HIGH);
    digitalWrite(in2, LOW);
 
    // Set Motor B forward
 
    digitalWrite(in3, HIGH);
    digitalWrite(in4, LOW);


    // the motor routines internally limit the argument to {-255, 255}
//    set_m1_speed(motorSpeed-bias); //left motor
 //   set_m2_speed(motorSpeed+bias); //right motor
    analogWrite(enA, (motorSpeed-bias));
    analogWrite(enB, (motorSpeed+bias));
    
  }
  else
  {
    stop1();
  }
    
}
void stop1()
{
  digitalWrite(in1,LOW);
  digitalWrite(in2,LOW);
  digitalWrite(in3,LOW);
  digitalWrite(in4,LOW);
}

[jremington]

The best test is in the water. I can't tell if your code will work correctly just by looking at it.

However, I CAN tell you that this comment is not correct for analogWrite(), and you will be in trouble if the motor speed values become negative:

// the motor routines internally limit the argument to {-255, 255}

I calibrated in accordance with Pololu recommendations and got the maximum and minimum values.

Magneto will do a better job.

[PYJTER]

I am aware that only tests on the water give the answer, I will try to go again next week, mark the target point and provide more reliable tests. But they predict the temperature -5;) fortunately, there is a discharge of warm water from the power plant, so the water will not freeze.
As for the magneto, I will try to find out how to apply this matrix in their script first.

However, I CAN tell you that this comment is not correct for analogWrite(), and you will be in trouble if the motor speed values become negative

I don't know if I understand correctly that the value (motorSpeed- + bias) can be negative. In this case, the motor should turn the other way.
Can I assume the dependence that if a given engine has a negative value then take 0, so it does nothing ??

Do you suggest any other solution instead of analogWrite () ??

Forgive my questions, but I am old and I do not assimilate knowledge so easily ... But I am determined to test in spite of adversities until I get the boat to the destination point in a straight line;) Thank you again for your help.

[jremington]

@PYJTER

I don't know if I understand correctly that the value (motorSpeed- + bias) can be negative. In this case, the motor should turn the other way.

That is up to you. analogWrite() won't work as you expect with a negative argument. It also won't work correctly if the argument exceeds 255. YOU MUST CHECK.

//    set_m1_speed(motorSpeed-bias); //left motor
 //   set_m2_speed(motorSpeed+bias); //right motor
    analogWrite(enA, (motorSpeed-bias));
    analogWrite(enB, (motorSpeed+bias));

[PYJTER]

Ok, I will add checking what is the value below 0 or above 255 so that it sets analogWrite () only in the range of 0-255. And I'll check it over the water and let you know how it turned out. Thanks for the help.

    motorSpeed1 = (motorSpeed - bias);
    motorSpeed2 = (motorSpeed + bias);
 
   
    if (motorSpeed1 < 0)motorSpeed1 = 0;
    if (motorSpeed2 > 255)motorSpeed2 = 255;