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Use accel of the same instance or primary baro for gyro instances that do not have valid temperature readings in temperature calibration data, use primary baro for magnetometers without valid temperature.

This commit is contained in:
mcsauder 2022-09-27 18:40:40 -06:00 committed by Daniel Agar
parent b8ad9bdbe5
commit af44da25f0
19 changed files with 814 additions and 717 deletions
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165
Tools/process_sensor_caldata.py
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@ -17,7 +17,7 @@ Reads in IMU data from a static thermal calibration test and performs a curve fi
Data can be gathered using the following sequence:
1) Power up the board and set the TC_A_ENABLE, TC_B_ENABLE and TC_G_ENABLE parameters to 1
2) Set all CAL_GYR and CAL_ACC parameters to defaults
2) Set all CAL_ACC*, CAL_GYR*, and CAL_MAG* parameters to defaults
3) Set the parameter SDLOG_MODE to 2, and SDLOG_PROFILE "Thermal calibration" bit (2) to enable logging of sensor data for calibration and power off
4) Cold soak the board for 30 minutes
5) Move to a warm dry, still air, constant pressure environment.
@ -31,7 +31,6 @@ Data can be gathered using the following sequence:
Outputs thermal compensation parameters in a file named <inputfilename>.params which can be loaded onto the board using QGroundControl
Outputs summary plots in a pdf file named <inputfilename>.pdf
"""
def resampleWithDeltaX(x,y):
@ -68,7 +67,7 @@ def resampleWithDeltaX(x,y):
def median_filter(data):
return medfilt(data, 31)
parser = argparse.ArgumentParser(description='Reads in IMU data from a static thermal calibration test and performs a curve fit of accel, gyro, mag, and baro bias vs temperature')
parser = argparse.ArgumentParser(description='Reads in IMU data from a static thermal calibration test and performs a curve fit of gyro, accel and baro bias vs temperature')
parser.add_argument('filename', metavar='file.ulg', help='ULog input file')
parser.add_argument('--no_resample', dest='noResample', action='store_const',
const=True, default=False, help='skip resampling and use raw data')
@ -210,6 +209,7 @@ if num_accels >= 1 and not math.isnan(sensor_accel_0['temperature'][0]):
accel_0_params['TC_A0_TMIN'] = np.amin(sensor_accel_0['temperature'])
accel_0_params['TC_A0_TMAX'] = np.amax(sensor_accel_0['temperature'])
accel_0_params['TC_A0_TREF'] = 0.5 * (accel_0_params['TC_A0_TMIN'] + accel_0_params['TC_A0_TMAX'])
temp_rel = sensor_accel_0['temperature'] - accel_0_params['TC_A0_TREF']
temp_rel_resample = np.linspace(accel_0_params['TC_A0_TMIN']-accel_0_params['TC_A0_TREF'], accel_0_params['TC_A0_TMAX']-accel_0_params['TC_A0_TREF'], 100)
temp_resample = temp_rel_resample + accel_0_params['TC_A0_TREF']
@ -325,6 +325,7 @@ if num_accels >= 2 and not math.isnan(sensor_accel_1['temperature'][0]):
accel_1_params['TC_A1_TMIN'] = np.amin(sensor_accel_1['temperature'])
accel_1_params['TC_A1_TMAX'] = np.amax(sensor_accel_1['temperature'])
accel_1_params['TC_A1_TREF'] = 0.5 * (accel_1_params['TC_A1_TMIN'] + accel_1_params['TC_A1_TMAX'])
temp_rel = sensor_accel_1['temperature'] - accel_1_params['TC_A1_TREF']
temp_rel_resample = np.linspace(accel_1_params['TC_A1_TMIN']-accel_1_params['TC_A1_TREF'], accel_1_params['TC_A1_TMAX']-accel_1_params['TC_A1_TREF'], 100)
temp_resample = temp_rel_resample + accel_1_params['TC_A1_TREF']
@ -441,6 +442,7 @@ if num_accels >= 3 and not math.isnan(sensor_accel_2['temperature'][0]):
accel_2_params['TC_A2_TMIN'] = np.amin(sensor_accel_2['temperature'])
accel_2_params['TC_A2_TMAX'] = np.amax(sensor_accel_2['temperature'])
accel_2_params['TC_A2_TREF'] = 0.5 * (accel_2_params['TC_A2_TMIN'] + accel_2_params['TC_A2_TMAX'])
temp_rel = sensor_accel_2['temperature'] - accel_2_params['TC_A2_TREF']
temp_rel_resample = np.linspace(accel_2_params['TC_A2_TMIN']-accel_2_params['TC_A2_TREF'], accel_2_params['TC_A2_TMAX']-accel_2_params['TC_A2_TREF'], 100)
temp_resample = temp_rel_resample + accel_2_params['TC_A2_TREF']
@ -556,6 +558,7 @@ if num_accels >= 4 and not math.isnan(sensor_accel_3['temperature'][0]):
accel_3_params['TC_A3_TMIN'] = np.amin(sensor_accel_3['temperature'])
accel_3_params['TC_A3_TMAX'] = np.amax(sensor_accel_3['temperature'])
accel_3_params['TC_A3_TREF'] = 0.5 * (accel_3_params['TC_A3_TMIN'] + accel_3_params['TC_A3_TMAX'])
temp_rel = sensor_accel_3['temperature'] - accel_3_params['TC_A3_TREF']
temp_rel_resample = np.linspace(accel_3_params['TC_A3_TMIN']-accel_3_params['TC_A3_TREF'], accel_3_params['TC_A3_TMAX']-accel_3_params['TC_A3_TREF'], 100)
temp_resample = temp_rel_resample + accel_3_params['TC_A3_TREF']
@ -650,13 +653,24 @@ gyro_0_params = {
}
# curve fit the data for gyro 0 corrections
if num_gyros >= 1 and not math.isnan(sensor_gyro_0['temperature'][0]):
if num_gyros >= 1:
#if the gyro has no temperature data, use the corresponding accel instance or the primary baro temp.
if math.isnan(sensor_gyro_0['temperature'][0]):
if not math.isnan(sensor_accel_0['temperature'][0]):
sensor_gyro_0['temperature'] = sensor_accel_0['temperature']
elif not math.isnan(sensor_baro_0['temperature'][0]):
sensor_gyro_0['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_gyro_0['temperature'][0]):
gyro_0_params['TC_G0_ID'] = int(np.median(sensor_gyro_0['device_id']))
# find the min, max and reference temperature
gyro_0_params['TC_G0_TMIN'] = np.amin(sensor_gyro_0['temperature'])
gyro_0_params['TC_G0_TMAX'] = np.amax(sensor_gyro_0['temperature'])
gyro_0_params['TC_G0_TREF'] = 0.5 * (gyro_0_params['TC_G0_TMIN'] + gyro_0_params['TC_G0_TMAX'])
temp_rel = sensor_gyro_0['temperature'] - gyro_0_params['TC_G0_TREF']
temp_rel_resample = np.linspace(gyro_0_params['TC_G0_TMIN']-gyro_0_params['TC_G0_TREF'], gyro_0_params['TC_G0_TMAX']-gyro_0_params['TC_G0_TREF'], 100)
temp_resample = temp_rel_resample + gyro_0_params['TC_G0_TREF']
@ -737,6 +751,7 @@ if num_gyros >= 1 and not math.isnan(sensor_gyro_0['temperature'][0]):
pp.savefig()
#################################################################################
#################################################################################
@ -762,13 +777,24 @@ gyro_1_params = {
}
# curve fit the data for gyro 1 corrections
if num_gyros >= 2 and not math.isnan(sensor_gyro_1['temperature'][0]):
if num_gyros >= 2:
#if the gyro has no temperature data, use the corresponding accel instance or the primary baro temp.
if math.isnan(sensor_gyro_1['temperature'][0]):
if not math.isnan(sensor_accel_1['temperature'][0]):
sensor_gyro_1['temperature'] = sensor_accel_1['temperature']
elif not math.isnan(sensor_baro_0['temperature'][0]):
sensor_gyro_1['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_gyro_1['temperature'][0]):
gyro_1_params['TC_G1_ID'] = int(np.median(sensor_gyro_1['device_id']))
# find the min, max and reference temperature
gyro_1_params['TC_G1_TMIN'] = np.amin(sensor_gyro_1['temperature'])
gyro_1_params['TC_G1_TMAX'] = np.amax(sensor_gyro_1['temperature'])
gyro_1_params['TC_G1_TREF'] = 0.5 * (gyro_1_params['TC_G1_TMIN'] + gyro_1_params['TC_G1_TMAX'])
temp_rel = sensor_gyro_1['temperature'] - gyro_1_params['TC_G1_TREF']
temp_rel_resample = np.linspace(gyro_1_params['TC_G1_TMIN']-gyro_1_params['TC_G1_TREF'], gyro_1_params['TC_G1_TMAX']-gyro_1_params['TC_G1_TREF'], 100)
temp_resample = temp_rel_resample + gyro_1_params['TC_G1_TREF']
@ -874,13 +900,24 @@ gyro_2_params = {
}
# curve fit the data for gyro 2 corrections
if num_gyros >= 3 and not math.isnan(sensor_gyro_2['temperature'][0]):
if num_gyros >= 3:
#if the gyro has no temperature data, use the corresponding accel instance or the primary baro temp.
if math.isnan(sensor_gyro_2['temperature'][0]):
if not math.isnan(sensor_accel_2['temperature'][0]):
sensor_gyro_2['temperature'] = sensor_accel_2['temperature']
elif not math.isnan(sensor_baro_0['temperature'][0]):
sensor_gyro_2['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_gyro_2['temperature'][0]):
gyro_2_params['TC_G2_ID'] = int(np.median(sensor_gyro_2['device_id']))
# find the min, max and reference temperature
gyro_2_params['TC_G2_TMIN'] = np.amin(sensor_gyro_2['temperature'])
gyro_2_params['TC_G2_TMAX'] = np.amax(sensor_gyro_2['temperature'])
gyro_2_params['TC_G2_TREF'] = 0.5 * (gyro_2_params['TC_G2_TMIN'] + gyro_2_params['TC_G2_TMAX'])
temp_rel = sensor_gyro_2['temperature'] - gyro_2_params['TC_G2_TREF']
temp_rel_resample = np.linspace(gyro_2_params['TC_G2_TMIN']-gyro_2_params['TC_G2_TREF'], gyro_2_params['TC_G2_TMAX']-gyro_2_params['TC_G2_TREF'], 100)
temp_resample = temp_rel_resample + gyro_2_params['TC_G2_TREF']
@ -986,14 +1023,25 @@ gyro_3_params = {
}
# curve fit the data for gyro 3 corrections
if num_gyros >= 4 and not math.isnan(sensor_gyro_3['temperature'][0]):
if num_gyros >= 4:
#if the gyro has no temperature data, use the corresponding accel instance or the primary baro temp.
if math.isnan(sensor_gyro_3['temperature'][0]):
if not math.isnan(sensor_accel_3['temperature'][0]):
sensor_gyro_3['temperature'] = sensor_accel_3['temperature']
elif not math.isnan(sensor_baro_0['temperature'][0]):
sensor_gyro_3['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_gyro_3['temperature'][0]):
gyro_3_params['TC_G3_ID'] = int(np.median(sensor_gyro_3['device_id']))
# find the min, max and reference temperature
gyro_3_params['TC_G3_TMIN'] = np.amin(sensor_gyro_3['temperature'])
gyro_3_params['TC_G3_TMAX'] = np.amax(sensor_gyro_3['temperature'])
gyro_3_params['TC_G3_TREF'] = 0.5 * (gyro_3_params['TC_G3_TMIN'] + gyro_3_params['TC_G3_TMAX'])
temp_rel = sensor_gyro_3['temperature'] - gyro_3_params['TC_G3_TREF']
gyro_3_params['TC_G3_TREF'] = 0.5 * (gyro_2_params['TC_G3_TMIN'] + gyro_2_params['TC_G3_TMAX'])
temp_rel = sensor_gyro_3['temperature'] - gyro_2_params['TC_G3_TREF']
temp_rel_resample = np.linspace(gyro_3_params['TC_G3_TMIN']-gyro_3_params['TC_G3_TREF'], gyro_3_params['TC_G3_TMAX']-gyro_3_params['TC_G3_TREF'], 100)
temp_resample = temp_rel_resample + gyro_3_params['TC_G3_TREF']
@ -1062,7 +1110,7 @@ if num_gyros >= 4 and not math.isnan(sensor_gyro_3['temperature'][0]):
#################################################################################
# define data dictionary of accel 0 thermal correction parameters
# define data dictionary of mag 0 thermal correction parameters
mag_0_params = {
'TC_M0_ID':0,
'TC_M0_TMIN':0.0,
@ -1082,14 +1130,19 @@ mag_0_params = {
'TC_M0_X3_2':0.0
}
# curve fit the data for accel 0 corrections
if num_mags >= 1 and not math.isnan(sensor_mag_0['temperature'][0]):
mag_0_params['TC_M0_ID'] = int(np.median(sensor_mag_0['device_id']))
# curve fit the data for mag 0 corrections
if num_mags >= 1:
if math.isnan(sensor_mag_0['temperature'][0]):
if not math.isnan(sensor_baro_0['temperature'][0]):
sensor_mag_0['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_mag_0['temperature'][0]):
# find the min, max and reference temperature
mag_0_params['TC_M0_TMIN'] = np.amin(sensor_mag_0['temperature'])
mag_0_params['TC_M0_TMAX'] = np.amax(sensor_mag_0['temperature'])
mag_0_params['TC_M0_TREF'] = 0.5 * (mag_0_params['TC_M0_TMIN'] + mag_0_params['TC_M0_TMAX'])
temp_rel = sensor_mag_0['temperature'] - mag_0_params['TC_M0_TREF']
temp_rel_resample = np.linspace(mag_0_params['TC_M0_TMIN']-mag_0_params['TC_M0_TREF'], mag_0_params['TC_M0_TMAX']-mag_0_params['TC_M0_TREF'], 100)
temp_resample = temp_rel_resample + mag_0_params['TC_M0_TREF']
@ -1100,6 +1153,7 @@ if num_mags >= 1 and not math.isnan(sensor_mag_0['temperature'][0]):
# fit X axis
correction_x = sensor_mag_0['x'] - np.median(sensor_mag_0['x'])
if noResample:
coef_mag_0_x = np.polyfit(temp_rel,correction_x, 3)
else:
@ -1110,11 +1164,13 @@ if num_mags >= 1 and not math.isnan(sensor_mag_0['temperature'][0]):
mag_0_params['TC_M0_X2_0'] = coef_mag_0_x[1]
mag_0_params['TC_M0_X1_0'] = coef_mag_0_x[2]
mag_0_params['TC_M0_X0_0'] = coef_mag_0_x[3]
fit_coef_mag_0_x = np.poly1d(coef_mag_0_x)
correction_x_resample = fit_coef_mag_0_x(temp_rel_resample)
# fit Y axis
correction_y = sensor_mag_0['y'] - np.median(sensor_mag_0['y'])
if noResample:
coef_mag_0_y = np.polyfit(temp_rel, correction_y, 3)
else:
@ -1125,11 +1181,13 @@ if num_mags >= 1 and not math.isnan(sensor_mag_0['temperature'][0]):
mag_0_params['TC_M0_X2_1'] = coef_mag_0_y[1]
mag_0_params['TC_M0_X1_1'] = coef_mag_0_y[2]
mag_0_params['TC_M0_X0_1'] = coef_mag_0_y[3]
fit_coef_mag_0_y = np.poly1d(coef_mag_0_y)
correction_y_resample = fit_coef_mag_0_y(temp_rel_resample)
# fit Z axis
correction_z = sensor_mag_0['z'] - np.median(sensor_mag_0['z'])
if noResample:
coef_mag_0_z = np.polyfit(temp_rel,correction_z, 3)
else:
@ -1140,6 +1198,7 @@ if num_mags >= 1 and not math.isnan(sensor_mag_0['temperature'][0]):
mag_0_params['TC_M0_X2_2'] = coef_mag_0_z[1]
mag_0_params['TC_M0_X1_2'] = coef_mag_0_z[2]
mag_0_params['TC_M0_X0_2'] = coef_mag_0_z[3]
fit_coef_mag_0_z = np.poly1d(coef_mag_0_z)
correction_z_resample = fit_coef_mag_0_z(temp_rel_resample)
@ -1198,13 +1257,20 @@ mag_1_params = {
}
# curve fit the data for mag 1 corrections
if num_mags >= 2 and not math.isnan(sensor_mag_1['temperature'][0]):
if num_mags >= 2:
if math.isnan(sensor_mag_1['temperature'][0]):
if not math.isnan(sensor_baro_0['temperature'][0]):
sensor_mag_1['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_mag_1['temperature'][0]):
mag_1_params['TC_M1_ID'] = int(np.median(sensor_mag_1['device_id']))
# find the min, max and reference temperature
mag_1_params['TC_M1_TMIN'] = np.amin(sensor_mag_1['temperature'])
mag_1_params['TC_M1_TMAX'] = np.amax(sensor_mag_1['temperature'])
mag_1_params['TC_M1_TREF'] = 0.5 * (mag_1_params['TC_M1_TMIN'] + mag_1_params['TC_M1_TMAX'])
temp_rel = sensor_mag_1['temperature'] - mag_1_params['TC_M1_TREF']
temp_rel_resample = np.linspace(mag_1_params['TC_M1_TMIN']-mag_1_params['TC_M1_TREF'], mag_1_params['TC_M1_TMAX']-mag_1_params['TC_M1_TREF'], 100)
temp_resample = temp_rel_resample + mag_1_params['TC_M1_TREF']
@ -1215,6 +1281,7 @@ if num_mags >= 2 and not math.isnan(sensor_mag_1['temperature'][0]):
# fit X axis
correction_x = sensor_mag_1['x'] - np.median(sensor_mag_1['x'])
if noResample:
coef_mag_1_x = np.polyfit(temp_rel, correction_x, 3)
else:
@ -1225,11 +1292,13 @@ if num_mags >= 2 and not math.isnan(sensor_mag_1['temperature'][0]):
mag_1_params['TC_M1_X2_0'] = coef_mag_1_x[1]
mag_1_params['TC_M1_X1_0'] = coef_mag_1_x[2]
mag_1_params['TC_M1_X0_0'] = coef_mag_1_x[3]
fit_coef_mag_1_x = np.poly1d(coef_mag_1_x)
correction_x_resample = fit_coef_mag_1_x(temp_rel_resample)
# fit Y axis
correction_y = sensor_mag_1['y'] - np.median(sensor_mag_1['y'])
if noResample:
coef_mag_1_y = np.polyfit(temp_rel,correction_y,3)
else:
@ -1240,11 +1309,13 @@ if num_mags >= 2 and not math.isnan(sensor_mag_1['temperature'][0]):
mag_1_params['TC_M1_X2_1'] = coef_mag_1_y[1]
mag_1_params['TC_M1_X1_1'] = coef_mag_1_y[2]
mag_1_params['TC_M1_X0_1'] = coef_mag_1_y[3]
fit_coef_mag_1_y = np.poly1d(coef_mag_1_y)
correction_y_resample = fit_coef_mag_1_y(temp_rel_resample)
# fit Z axis
correction_z = sensor_mag_1['z'] - np.median(sensor_mag_1['z'])
if noResample:
coef_mag_1_z = np.polyfit(temp_rel,correction_z, 3)
else:
@ -1255,6 +1326,7 @@ if num_mags >= 2 and not math.isnan(sensor_mag_1['temperature'][0]):
mag_1_params['TC_M1_X2_2'] = coef_mag_1_z[1]
mag_1_params['TC_M1_X1_2'] = coef_mag_1_z[2]
mag_1_params['TC_M1_X0_2'] = coef_mag_1_z[3]
fit_coef_mag_1_z = np.poly1d(coef_mag_1_z)
correction_z_resample = fit_coef_mag_1_z(temp_rel_resample)
@ -1314,13 +1386,20 @@ mag_2_params = {
}
# curve fit the data for mag 2 corrections
if num_mags >= 3 and not math.isnan(sensor_mag_2['temperature'][0]):
if num_mags >= 3:
if math.isnan(sensor_mag_2['temperature'][0]):
if not math.isnan(sensor_baro_0['temperature'][0]):
sensor_mag_2['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_mag_2['temperature'][0]):
mag_2_params['TC_M2_ID'] = int(np.median(sensor_mag_2['device_id']))
# find the min, max and reference temperature
mag_2_params['TC_M2_TMIN'] = np.amin(sensor_mag_2['temperature'])
mag_2_params['TC_M2_TMAX'] = np.amax(sensor_mag_2['temperature'])
mag_2_params['TC_M2_TREF'] = 0.5 * (mag_2_params['TC_M2_TMIN'] + mag_2_params['TC_M2_TMAX'])
temp_rel = sensor_mag_2['temperature'] - mag_2_params['TC_M2_TREF']
temp_rel_resample = np.linspace(mag_2_params['TC_M2_TMIN']-mag_2_params['TC_M2_TREF'], mag_2_params['TC_M2_TMAX']-mag_2_params['TC_M2_TREF'], 100)
temp_resample = temp_rel_resample + mag_2_params['TC_M2_TREF']
@ -1331,6 +1410,7 @@ if num_mags >= 3 and not math.isnan(sensor_mag_2['temperature'][0]):
# fit X axis
correction_x = sensor_mag_2['x'] - np.median(sensor_mag_2['x'])
if noResample:
coef_mag_2_x = np.polyfit(temp_rel,correction_x, 3)
else:
@ -1341,11 +1421,13 @@ if num_mags >= 3 and not math.isnan(sensor_mag_2['temperature'][0]):
mag_2_params['TC_M2_X2_0'] = coef_mag_2_x[1]
mag_2_params['TC_M2_X1_0'] = coef_mag_2_x[2]
mag_2_params['TC_M2_X0_0'] = coef_mag_2_x[3]
fit_coef_mag_2_x = np.poly1d(coef_mag_2_x)
correction_x_resample = fit_coef_mag_2_x(temp_rel_resample)
# fit Y axis
correction_y = sensor_mag_2['y'] - np.median(sensor_mag_2['y'])
if noResample:
coef_mag_2_y = np.polyfit(temp_rel,correction_y,3)
else:
@ -1356,11 +1438,13 @@ if num_mags >= 3 and not math.isnan(sensor_mag_2['temperature'][0]):
mag_2_params['TC_M2_X2_1'] = coef_mag_2_y[1]
mag_2_params['TC_M2_X1_1'] = coef_mag_2_y[2]
mag_2_params['TC_M2_X0_1'] = coef_mag_2_y[3]
fit_coef_mag_2_y = np.poly1d(coef_mag_2_y)
correction_y_resample = fit_coef_mag_2_y(temp_rel_resample)
# fit Z axis
correction_z = sensor_mag_2['z'] - np.median(sensor_mag_2['z'])
if noResample:
coef_mag_2_z = np.polyfit(temp_rel,correction_z,3)
else:
@ -1371,6 +1455,7 @@ if num_mags >= 3 and not math.isnan(sensor_mag_2['temperature'][0]):
mag_2_params['TC_M2_X2_2'] = coef_mag_2_z[1]
mag_2_params['TC_M2_X1_2'] = coef_mag_2_z[2]
mag_2_params['TC_M2_X0_2'] = coef_mag_2_z[3]
fit_coef_mag_2_z = np.poly1d(coef_mag_2_z)
correction_z_resample = fit_coef_mag_2_z(temp_rel_resample)
@ -1428,49 +1513,81 @@ mag_3_params = {
'TC_M3_X3_2':0.0
}
# curve fit the data for mag 2 corrections
if num_mags >= 4 and not math.isnan(sensor_mag_3['temperature'][0]):
# curve fit the data for mag 3 corrections
if num_mags >= 4:
if math.isnan(sensor_mag_3['temperature'][0]):
if not math.isnan(sensor_baro_0['temperature'][0]):
sensor_mag_3['temperature'] = sensor_baro_0['temperature']
if not math.isnan(sensor_mag_3['temperature'][0]):
mag_3_params['TC_M3_ID'] = int(np.median(sensor_mag_3['device_id']))
# find the min, max and reference temperature
mag_3_params['TC_M3_TMIN'] = np.amin(sensor_mag_3['temperature'])
mag_3_params['TC_M3_TMAX'] = np.amax(sensor_mag_3['temperature'])
mag_3_params['TC_M3_TREF'] = 0.5 * (mag_3_params['TC_M3_TMIN'] + mag_3_params['TC_M3_TMAX'])
temp_rel = sensor_mag_3['temperature'] - mag_3_params['TC_M3_TREF']
temp_rel_resample = np.linspace(mag_3_params['TC_M3_TMIN']-mag_3_params['TC_M3_TREF'], mag_3_params['TC_M3_TMAX']-mag_3_params['TC_M3_TREF'], 100)
temp_resample = temp_rel_resample + mag_3_params['TC_M3_TREF']
# Delete the dataset first and last 10 seconds of data
sensor_mag_3 = np.delete(sensor_mag_3, range(0,1000), axis=['x'])
sensor_mag_3 = np.delete(sensor_mag_3, range(-1000,), axis=['x'])
sensor_mag_3['x'] = median_filter(sensor_mag_3['x'])
sensor_mag_3['y'] = median_filter(sensor_mag_3['y'])
sensor_mag_3['z'] = median_filter(sensor_mag_3['z'])
# fit X axis
correction_x = sensor_mag_3['x'] - np.median(sensor_mag_3['x'])
coef_mag_3_x = np.polyfit(temp_rel, correction_x, 3)
if noResample:
coef_mag_3_x = np.polyfit(temp_rel,correction_x, 3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_x)
coef_mag_3_x = np.polyfit(temp, sens, 3)
mag_3_params['TC_M3_X3_0'] = coef_mag_3_x[0]
mag_3_params['TC_M3_X2_0'] = coef_mag_3_x[1]
mag_3_params['TC_M3_X1_0'] = coef_mag_3_x[2]
mag_3_params['TC_M3_X0_0'] = coef_mag_3_x[3]
fit_coef_mag_3_x = np.poly1d(coef_mag_3_x)
correction_x_resample = fit_coef_mag_3_x(temp_rel_resample)
# fit Y axis
correction_y = sensor_mag_3['y'] - np.median(sensor_mag_3['y'])
if noResample:
coef_mag_3_y = np.polyfit(temp_rel, correction_y, 3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_y)
coef_mag_3_y = np.polyfit(temp, sens, 3)
mag_3_params['TC_M3_X3_1'] = coef_mag_3_y[0]
mag_3_params['TC_M3_X2_1'] = coef_mag_3_y[1]
mag_3_params['TC_M3_X1_1'] = coef_mag_3_y[2]
mag_3_params['TC_M3_X0_1'] = coef_mag_3_y[3]
fit_coef_mag_3_y = np.poly1d(coef_mag_3_y)
correction_y_resample = fit_coef_mag_3_y(temp_rel_resample)
# fit Z axis
correction_z = sensor_mag_3['z'] - np.median(sensor_mag_3['z'])
coef_mag_3_z = np.polyfit(temp_rel, correction_z, 3)
if noResample:
coef_mag_3_z = np.polyfit(temp_rel,correction_z, 3)
else:
temp, sens = resampleWithDeltaX(temp_rel,correction_z)
coef_mag_3_z = np.polyfit(temp, sens, 3)
mag_3_params['TC_M3_X3_2'] = coef_mag_3_z[0]
mag_3_params['TC_M3_X2_2'] = coef_mag_3_z[1]
mag_3_params['TC_M3_X1_2'] = coef_mag_3_z[2]
mag_3_params['TC_M3_X0_2'] = coef_mag_3_z[3]
fit_coef_mag_3_z = np.poly1d(coef_mag_3_z)
correction_z_resample = fit_coef_mag_3_z(temp_rel_resample)
@ -1529,6 +1646,7 @@ baro_0_params['TC_B0_ID'] = int(np.median(sensor_baro_0['device_id']))
baro_0_params['TC_B0_TMIN'] = np.amin(sensor_baro_0['temperature'])
baro_0_params['TC_B0_TMAX'] = np.amax(sensor_baro_0['temperature'])
baro_0_params['TC_B0_TREF'] = 0.5 * (baro_0_params['TC_B0_TMIN'] + baro_0_params['TC_B0_TMAX'])
temp_rel = sensor_baro_0['temperature'] - baro_0_params['TC_B0_TREF']
temp_rel_resample = np.linspace(baro_0_params['TC_B0_TMIN']-baro_0_params['TC_B0_TREF'], baro_0_params['TC_B0_TMAX']-baro_0_params['TC_B0_TREF'], 100)
temp_resample = temp_rel_resample + baro_0_params['TC_B0_TREF']
@ -1549,6 +1667,7 @@ baro_0_params['TC_B0_X3'] = coef_baro_0_x[2]
baro_0_params['TC_B0_X2'] = coef_baro_0_x[3]
baro_0_params['TC_B0_X1'] = coef_baro_0_x[4]
baro_0_params['TC_B0_X0'] = coef_baro_0_x[5]
fit_coef_baro_0_x = np.poly1d(coef_baro_0_x)
baro_0_x_resample = fit_coef_baro_0_x(temp_rel_resample)
@ -1588,6 +1707,7 @@ if num_baros >= 2:
baro_1_params['TC_B1_TMIN'] = np.amin(sensor_baro_1['temperature'])
baro_1_params['TC_B1_TMAX'] = np.amax(sensor_baro_1['temperature'])
baro_1_params['TC_B1_TREF'] = 0.5 * (baro_1_params['TC_B1_TMIN'] + baro_1_params['TC_B1_TMAX'])
temp_rel = sensor_baro_1['temperature'] - baro_1_params['TC_B1_TREF']
temp_rel_resample = np.linspace(baro_1_params['TC_B1_TMIN']-baro_1_params['TC_B1_TREF'], baro_1_params['TC_B1_TMAX']-baro_1_params['TC_B1_TREF'], 100)
temp_resample = temp_rel_resample + baro_1_params['TC_B1_TREF']
@ -1608,6 +1728,7 @@ if num_baros >= 2:
baro_1_params['TC_B1_X2'] = coef_baro_1_x[3]
baro_1_params['TC_B1_X1'] = coef_baro_1_x[4]
baro_1_params['TC_B1_X0'] = coef_baro_1_x[5]
fit_coef_baro_1_x = np.poly1d(coef_baro_1_x)
baro_1_x_resample = fit_coef_baro_1_x(temp_rel_resample)
@ -1636,7 +1757,6 @@ baro_2_params = {
'TC_B2_X3':0.0,
'TC_B2_X4':0.0,
'TC_B2_X5':0.0,
'TC_B2_SCL':1.0,
}
if num_baros >= 3:
@ -1648,6 +1768,7 @@ if num_baros >= 3:
baro_2_params['TC_B2_TMIN'] = np.amin(sensor_baro_2['temperature'])
baro_2_params['TC_B2_TMAX'] = np.amax(sensor_baro_2['temperature'])
baro_2_params['TC_B2_TREF'] = 0.5 * (baro_2_params['TC_B2_TMIN'] + baro_2_params['TC_B2_TMAX'])
temp_rel = sensor_baro_2['temperature'] - baro_2_params['TC_B2_TREF']
temp_rel_resample = np.linspace(baro_2_params['TC_B2_TMIN']-baro_2_params['TC_B2_TREF'], baro_2_params['TC_B2_TMAX']-baro_2_params['TC_B2_TREF'], 100)
temp_resample = temp_rel_resample + baro_2_params['TC_B2_TREF']
@ -1668,6 +1789,7 @@ if num_baros >= 3:
baro_2_params['TC_B2_X2'] = coef_baro_2_x[3]
baro_2_params['TC_B2_X1'] = coef_baro_2_x[4]
baro_2_params['TC_B2_X0'] = coef_baro_2_x[5]
fit_coef_baro_2_x = np.poly1d(coef_baro_2_x)
baro_2_x_resample = fit_coef_baro_2_x(temp_rel_resample)
@ -1696,7 +1818,6 @@ baro_3_params = {
'TC_B3_X3':0.0,
'TC_B3_X4':0.0,
'TC_B3_X5':0.0,
'TC_B3_SCL':1.0,
}
if num_baros >= 4:
@ -1708,6 +1829,7 @@ if num_baros >= 4:
baro_3_params['TC_B3_TMIN'] = np.amin(sensor_baro_3['temperature'])
baro_3_params['TC_B3_TMAX'] = np.amax(sensor_baro_3['temperature'])
baro_3_params['TC_B3_TREF'] = 0.5 * (baro_3_params['TC_B3_TMIN'] + baro_3_params['TC_B3_TMAX'])
temp_rel = sensor_baro_3['temperature'] - baro_3_params['TC_B3_TREF']
temp_rel_resample = np.linspace(baro_3_params['TC_B3_TMIN']-baro_3_params['TC_B3_TREF'], baro_3_params['TC_B3_TMAX']-baro_3_params['TC_B3_TREF'], 100)
temp_resample = temp_rel_resample + baro_3_params['TC_B3_TREF']
@ -1723,6 +1845,7 @@ if num_baros >= 4:
baro_3_params['TC_B3_X2'] = coef_baro_3_x[3]
baro_3_params['TC_B3_X1'] = coef_baro_3_x[4]
baro_3_params['TC_B3_X0'] = coef_baro_3_x[5]
fit_coef_baro_3_x = np.poly1d(coef_baro_3_x)
baro_3_x_resample = fit_coef_baro_3_x(temp_rel_resample)

19
src/drivers/magnetometer/isentek/ist8308/IST8308.cpp
View File

@ -220,13 +220,6 @@ void IST8308::RunImpl()
perf_count(_bad_register_perf);
Reset();
}
} else {
// periodically update temperature (~1 Hz)
if (hrt_elapsed_time(&_temperature_update_timestamp) >= 1_s) {
UpdateTemperature();
_temperature_update_timestamp = now;
}
}
}
@ -298,15 +291,3 @@ void IST8308::RegisterSetAndClearBits(Register reg, uint8_t setbits, uint8_t cle
RegisterWrite(reg, val);
}
}
void IST8308::UpdateTemperature()
{
// Isentek magnetometers do not have a temperature sensor, so we will to use the baro's value.
sensor_baro_s sensor_baro;
if (_sensor_baro_sub[0].update(&sensor_baro)) {
if (PX4_ISFINITE(sensor_baro.temperature)) {
_px4_mag.set_temperature(sensor_baro.temperature);
}
}
}

28
src/drivers/magnetometer/isentek/ist8308/IST8308.hpp
View File

@ -46,10 +46,7 @@
#include <lib/drivers/device/i2c.h>
#include <lib/drivers/magnetometer/PX4Magnetometer.hpp>
#include <lib/perf/perf_counter.h>
#include <lib/sensor_calibration/Barometer.hpp>
#include <px4_platform_common/i2c_spi_buses.h>
#include <uORB/SubscriptionMultiArray.hpp>
#include <uORB/topics/sensor_baro.h>
using namespace iSentek_IST8308;
@ -64,17 +61,9 @@ public:
void RunImpl();
int init() override;
void print_status() override;
private:
enum class STATE : uint8_t {
RESET,
WAIT_FOR_RESET,
CONFIGURE,
READ,
} _state{STATE::RESET};
// Sensor Configuration
struct register_config_t {
Register reg;
@ -91,15 +80,9 @@ private:
bool RegisterCheck(const register_config_t &reg_cfg);
uint8_t RegisterRead(Register reg);
void RegisterWrite(Register reg, uint8_t value);
void RegisterSetAndClearBits(Register reg, uint8_t setbits, uint8_t clearbits);
void UpdateTemperature();
uORB::SubscriptionMultiArray<sensor_baro_s, calibration::Barometer::MAX_SENSOR_COUNT> _sensor_baro_sub{ORB_ID::sensor_baro};
PX4Magnetometer _px4_mag;
perf_counter_t _bad_register_perf{perf_alloc(PC_COUNT, MODULE_NAME": bad register")};
@ -108,14 +91,17 @@ private:
hrt_abstime _reset_timestamp{0};
hrt_abstime _last_config_check_timestamp{0};
hrt_abstime _temperature_update_timestamp{0};
int _failure_count{0};
enum class STATE : uint8_t {
RESET,
WAIT_FOR_RESET,
CONFIGURE,
READ,
} _state{STATE::RESET};
uint8_t _checked_register{0};
static constexpr uint8_t size_register_cfg{6};
register_config_t _register_cfg[size_register_cfg] {
// Register | Set bits, Clear bits
{ Register::ACTR, 0, ACTR_BIT::SUSPEND_EN },

20
src/drivers/magnetometer/isentek/ist8310/IST8310.cpp
View File

@ -188,7 +188,6 @@ void IST8310::RunImpl()
_px4_mag.set_error_count(perf_event_count(_bad_register_perf) + perf_event_count(_bad_transfer_perf));
_px4_mag.update(now, x, y, z);
UpdateTemperature();
success = true;
@ -223,13 +222,6 @@ void IST8310::RunImpl()
Reset();
return;
}
} else {
// periodically update temperature (~1 Hz)
if (hrt_elapsed_time(&_temperature_update_timestamp) >= 1_s) {
UpdateTemperature();
_temperature_update_timestamp = now;
}
}
// initiate next measurement
@ -304,15 +296,3 @@ void IST8310::RegisterSetAndClearBits(Register reg, uint8_t setbits, uint8_t cle
RegisterWrite(reg, val);
}
}
void IST8310::UpdateTemperature()
{
// Isentek magnetometers do not have a temperature sensor, so we will to use the baro's value.
sensor_baro_s sensor_baro;
if (_sensor_baro_sub[0].update(&sensor_baro)) {
if (PX4_ISFINITE(sensor_baro.temperature)) {
_px4_mag.set_temperature(sensor_baro.temperature);
}
}
}

30
src/drivers/magnetometer/isentek/ist8310/IST8310.hpp
View File

@ -46,10 +46,7 @@
#include <lib/drivers/device/i2c.h>
#include <lib/drivers/magnetometer/PX4Magnetometer.hpp>
#include <lib/perf/perf_counter.h>
#include <lib/sensor_calibration/Barometer.hpp>
#include <px4_platform_common/i2c_spi_buses.h>
#include <uORB/SubscriptionMultiArray.hpp>
#include <uORB/topics/sensor_baro.h>
using namespace iSentek_IST8310;
@ -64,18 +61,9 @@ public:
void RunImpl();
int init() override;
void print_status() override;
private:
enum class STATE : uint8_t {
RESET,
WAIT_FOR_RESET,
CONFIGURE,
MEASURE,
READ,
} _state{STATE::RESET};
// Sensor Configuration
struct register_config_t {
Register reg;
@ -92,15 +80,9 @@ private:
bool RegisterCheck(const register_config_t &reg_cfg);
uint8_t RegisterRead(Register reg);
void RegisterWrite(Register reg, uint8_t value);
void RegisterSetAndClearBits(Register reg, uint8_t setbits, uint8_t clearbits);
void UpdateTemperature();
uORB::SubscriptionMultiArray<sensor_baro_s, calibration::Barometer::MAX_SENSOR_COUNT> _sensor_baro_sub{ORB_ID::sensor_baro};
PX4Magnetometer _px4_mag;
perf_counter_t _bad_register_perf{perf_alloc(PC_COUNT, MODULE_NAME": bad register")};
@ -109,14 +91,18 @@ private:
hrt_abstime _reset_timestamp{0};
hrt_abstime _last_config_check_timestamp{0};
hrt_abstime _temperature_update_timestamp{0};
int _failure_count{0};
enum class STATE : uint8_t {
RESET,
WAIT_FOR_RESET,
CONFIGURE,
MEASURE,
READ,
} _state{STATE::RESET};
uint8_t _checked_register{0};
static constexpr uint8_t size_register_cfg{4};
register_config_t _register_cfg[size_register_cfg] {
// Register | Set bits, Clear bits
{ Register::CNTL2, 0, CNTL2_BIT::SRST },

12
src/modules/logger/logged_topics.cpp
View File

@ -344,17 +344,17 @@ void LoggedTopics::add_estimator_replay_topics()
void LoggedTopics::add_thermal_calibration_topics()
{
add_topic_multi("sensor_accel", 100, 3);
add_topic_multi("sensor_baro", 100, 3);
add_topic_multi("sensor_gyro", 100, 3);
add_topic_multi("sensor_accel", 100, 4);
add_topic_multi("sensor_baro", 100, 4);
add_topic_multi("sensor_gyro", 100, 4);
add_topic_multi("sensor_mag", 100, 4);
}
void LoggedTopics::add_sensor_comparison_topics()
{
add_topic_multi("sensor_accel", 100, 3);
add_topic_multi("sensor_baro", 100, 3);
add_topic_multi("sensor_gyro", 100, 3);
add_topic_multi("sensor_accel", 100, 4);
add_topic_multi("sensor_baro", 100, 4);
add_topic_multi("sensor_gyro", 100, 4);
add_topic_multi("sensor_mag", 100, 4);
}

6
src/modules/sensors/vehicle_acceleration/VehicleAcceleration.cpp
View File

@ -215,10 +215,12 @@ void VehicleAcceleration::Run()
// update corrections first to set _selected_sensor
bool selection_updated = SensorSelectionUpdate();
_calibration.SensorCorrectionsUpdate(selection_updated);
SensorBiasUpdate(selection_updated);
ParametersUpdate();
_calibration.SensorCorrectionsUpdate(selection_updated);
SensorBiasUpdate(selection_updated);
// require valid sensor sample rate to run
if (!PX4_ISFINITE(_filter_sample_rate)) {
CheckAndUpdateFilters();

5
src/modules/sensors/vehicle_angular_velocity/VehicleAngularVelocity.cpp
View File

@ -790,6 +790,8 @@ void VehicleAngularVelocity::Run()
const hrt_abstime time_now_us = hrt_absolute_time();
ParametersUpdate();
// update corrections first to set _selected_sensor
const bool selection_updated = SensorSelectionUpdate(time_now_us);
@ -801,9 +803,8 @@ void VehicleAngularVelocity::Run()
}
}
ParametersUpdate();
_calibration.SensorCorrectionsUpdate(selection_updated);
SensorBiasUpdate(selection_updated);
if (_reset_filters) {

16
src/modules/sensors/vehicle_magnetometer/VehicleMagnetometer.cpp
View File

@ -369,8 +369,8 @@ void VehicleMagnetometer::UpdatePowerCompensation()
if (_vehicle_thrust_setpoint_0_sub.update(&vehicle_thrust_setpoint)) {
const matrix::Vector3f thrust_setpoint = matrix::Vector3f(vehicle_thrust_setpoint.xyz);
for (auto &cal : _calibration) {
cal.UpdatePower(thrust_setpoint.length());
for (int i = 0; i < MAX_SENSOR_COUNT; i++) {
_calibration[i].UpdatePower(thrust_setpoint.length());
}
}
@ -382,14 +382,14 @@ void VehicleMagnetometer::UpdatePowerCompensation()
if (_battery_status_sub.update(&bat_stat)) {
float power = bat_stat.current_a * 0.001f; // current in [kA]
for (auto &cal : _calibration) {
cal.UpdatePower(power);
for (int i = 0; i < MAX_SENSOR_COUNT; i++) {
_calibration[i].UpdatePower(power);
}
}
} else {
for (auto &cal : _calibration) {
cal.UpdatePower(0.f);
for (int i = 0; i < MAX_SENSOR_COUNT; i++) {
_calibration[i].UpdatePower(0.f);
}
}
}
@ -412,6 +412,10 @@ void VehicleMagnetometer::Run()
}
}
for (int i = 0; i < MAX_SENSOR_COUNT; i++) {
_calibration[i].SensorCorrectionsUpdate();
}
UpdatePowerCompensation();
UpdateMagBiasEstimate();

2
src/modules/sensors/vehicle_magnetometer/VehicleMagnetometer.hpp
View File

@ -146,6 +146,7 @@ private:
Current_inst0,
Current_inst1
};
MagCompensationType _mag_comp_type{MagCompensationType::Disabled};
perf_counter_t _cycle_perf{perf_alloc(PC_ELAPSED, MODULE_NAME": cycle")};
@ -158,6 +159,7 @@ private:
uint64_t _timestamp_sample_sum[MAX_SENSOR_COUNT] {};
matrix::Vector3f _data_sum[MAX_SENSOR_COUNT] {};
int _data_sum_count[MAX_SENSOR_COUNT] {};
hrt_abstime _last_publication_timestamp[MAX_SENSOR_COUNT] {};

88
src/modules/temperature_compensation/TemperatureCompensationModule.cpp
View File

@ -144,16 +144,16 @@ void TemperatureCompensationModule::accelPoll()
// For each accel instance
for (uint8_t uorb_index = 0; uorb_index < ACCEL_COUNT_MAX; uorb_index++) {
sensor_accel_s report;
sensor_accel_s sensor_accel;
// Grab temperature from report
if (_accel_subs[uorb_index].update(&report)) {
if (PX4_ISFINITE(report.temperature)) {
// Grab temperature from accel
if (_accel_subs[uorb_index].update(&sensor_accel)) {
if (PX4_ISFINITE(sensor_accel.temperature)) {
// Update the offsets and mark for publication if they've changed
if (_temperature_compensation.update_offsets_accel(uorb_index, report.temperature, offsets[uorb_index]) == 2) {
if (_temperature_compensation.update_offsets_accel(uorb_index, sensor_accel.temperature, offsets[uorb_index]) == 2) {
_corrections.accel_device_ids[uorb_index] = report.device_id;
_corrections.accel_temperature[uorb_index] = report.temperature;
_corrections.accel_device_ids[uorb_index] = sensor_accel.device_id;
_corrections.accel_temperature[uorb_index] = sensor_accel.temperature;
_corrections_changed = true;
}
}
@ -167,16 +167,28 @@ void TemperatureCompensationModule::gyroPoll()
// For each gyro instance
for (uint8_t uorb_index = 0; uorb_index < GYRO_COUNT_MAX; uorb_index++) {
sensor_gyro_s report;
sensor_gyro_s sensor_gyro;
// Grab temperature from report
if (_gyro_subs[uorb_index].update(&report)) {
if (PX4_ISFINITE(report.temperature)) {
// Grab temperature from gyro
if (_gyro_subs[uorb_index].update(&sensor_gyro)) {
if (PX4_ISFINITE(sensor_gyro.temperature)) {
// Update the offsets and mark for publication if they've changed
if (_temperature_compensation.update_offsets_gyro(uorb_index, report.temperature, offsets[uorb_index]) == 2) {
if (_temperature_compensation.update_offsets_gyro(uorb_index, sensor_gyro.temperature, offsets[uorb_index]) == 2) {
_corrections.gyro_device_ids[uorb_index] = report.device_id;
_corrections.gyro_temperature[uorb_index] = report.temperature;
_corrections.gyro_device_ids[uorb_index] = sensor_gyro.device_id;
_corrections.gyro_temperature[uorb_index] = sensor_gyro.temperature;
_corrections_changed = true;
}
} else {
_corrections.gyro_device_ids[uorb_index] = sensor_gyro.device_id;
// Use accelerometer of the same instance if gyro temperature was NAN.
sensor_accel_s sensor_accel;
if (_accel_subs[uorb_index].update(&sensor_accel)) {
_corrections.gyro_temperature[uorb_index] = sensor_accel.temperature;
_corrections_changed = true;
}
}
@ -190,16 +202,28 @@ void TemperatureCompensationModule::magPoll()
// For each mag instance
for (uint8_t uorb_index = 0; uorb_index < MAG_COUNT_MAX; uorb_index++) {
sensor_mag_s report;
sensor_mag_s sensor_mag;
// Grab temperature from report
if (_mag_subs[uorb_index].update(&report)) {
if (PX4_ISFINITE(report.temperature)) {
if (_mag_subs[uorb_index].update(&sensor_mag)) {
if (PX4_ISFINITE(sensor_mag.temperature)) {
// Update the offsets and mark for publication if they've changed
if (_temperature_compensation.update_offsets_mag(uorb_index, report.temperature, offsets[uorb_index]) == 2) {
if (_temperature_compensation.update_offsets_mag(uorb_index, sensor_mag.temperature, offsets[uorb_index]) == 2) {
_corrections.mag_device_ids[uorb_index] = report.device_id;
_corrections.mag_temperature[uorb_index] = report.temperature;
_corrections.mag_device_ids[uorb_index] = sensor_mag.device_id;
_corrections.mag_temperature[uorb_index] = sensor_mag.temperature;
_corrections_changed = true;
}
} else {
_corrections.mag_device_ids[uorb_index] = sensor_mag.device_id;
// Use primary baro instance if mag temperature was NAN.
sensor_baro_s sensor_baro;
if (_accel_subs[0].update(&sensor_baro)) {
_corrections.mag_temperature[uorb_index] = sensor_baro.temperature;
_corrections_changed = true;
}
}
@ -213,16 +237,28 @@ void TemperatureCompensationModule::baroPoll()
// For each baro instance
for (uint8_t uorb_index = 0; uorb_index < BARO_COUNT_MAX; uorb_index++) {
sensor_baro_s report;
sensor_baro_s sensor_baro;
// Grab temperature from report
if (_baro_subs[uorb_index].update(&report)) {
if (PX4_ISFINITE(report.temperature)) {
if (_baro_subs[uorb_index].update(&sensor_baro)) {
if (PX4_ISFINITE(sensor_baro.temperature)) {
// Update the offsets and mark for publication if they've changed
if (_temperature_compensation.update_offsets_baro(uorb_index, report.temperature, offsets[uorb_index]) == 2) {
if (_temperature_compensation.update_offsets_baro(uorb_index, sensor_baro.temperature, offsets[uorb_index]) == 2) {
_corrections.baro_device_ids[uorb_index] = report.device_id;
_corrections.baro_temperature[uorb_index] = report.temperature;
_corrections.baro_device_ids[uorb_index] = sensor_baro.device_id;
_corrections.baro_temperature[uorb_index] = sensor_baro.temperature;
_corrections_changed = true;
}
} else {
_corrections.baro_device_ids[uorb_index] = sensor_baro.device_id;
// Use primary accelerometer instance if baro temperature was NAN.
sensor_accel_s sensor_accel;
if (_accel_subs[0].update(&sensor_accel)) {
_corrections.baro_temperature[uorb_index] = sensor_accel.temperature;
_corrections_changed = true;
}
}

2
src/modules/temperature_compensation/temp_comp_params_mag.c
View File

@ -1,6 +1,6 @@
/****************************************************************************
*
* Copyright (c) 2022 PX4 Development Team. All rights reserved.
* Copyright (c) 2022-2023 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions

2
src/modules/temperature_compensation/temp_comp_params_mag_0.c
View File

@ -1,6 +1,6 @@
/****************************************************************************
*
* Copyright (c) 2017-2020 PX4 Development Team. All rights reserved.
* Copyright (c) 2022-2023 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions

2
src/modules/temperature_compensation/temp_comp_params_mag_1.c
View File

@ -1,6 +1,6 @@
/****************************************************************************
*
* Copyright (c) 2017-2020 PX4 Development Team. All rights reserved.
* Copyright (c) 2022-2023 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions

2
src/modules/temperature_compensation/temp_comp_params_mag_2.c
View File

@ -1,6 +1,6 @@
/****************************************************************************
*
* Copyright (c) 2017-2020 PX4 Development Team. All rights reserved.
* Copyright (c) 2022-2023 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions

2
src/modules/temperature_compensation/temp_comp_params_mag_3.c
View File

@ -1,6 +1,6 @@
/****************************************************************************
*
* Copyright (c) 2017-2020 PX4 Development Team. All rights reserved.
* Copyright (c) 2022-2023 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions

2
src/modules/temperature_compensation/temperature_calibration/mag.cpp
View File

@ -1,6 +1,6 @@
/****************************************************************************
*
* Copyright (c) 2017 PX4 Development Team. All rights reserved.
* Copyright (c) 2022-2023 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions

2
src/modules/temperature_compensation/temperature_calibration/mag.h
View File

@ -1,6 +1,6 @@
/****************************************************************************
*
* Copyright (c) 2017 PX4 Development Team. All rights reserved.
* Copyright (c) 2022-2023 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions

8
src/modules/temperature_compensation/temperature_calibration/task.cpp
View File

@ -69,17 +69,13 @@ class TemperatureCalibration
{
public:
TemperatureCalibration(bool accel, bool gyro, bool mag, bool baro)
: _accel(accel)
, _gyro(gyro)
, _mag(mag)
, _baro(baro) {};
TemperatureCalibration(bool accel, bool gyro, bool mag, bool baro) :
_accel(accel), _gyro(gyro), _mag(mag), _baro(baro) {};
~TemperatureCalibration() = default;
/**
* Start task.
*
* @return OK on success.
*/
int start();