ardupilot/Tools/scripts/tempcal_IMU.py

524 lines
20 KiB
Python
Executable File

#!/usr/bin/env python
'''
Create temperature calibration parameters for IMUs based on log data.
'''
from argparse import ArgumentParser
parser = ArgumentParser(description=__doc__)
parser.add_argument("--outfile", default="tcal.parm", help='set output file')
parser.add_argument("--no-graph", action='store_true', default=False, help='disable graph display')
parser.add_argument("--log-parm", action='store_true', default=False, help='show corrections using coefficients from log file')
parser.add_argument("--online", action='store_true', default=False, help='use online polynomial fitting')
parser.add_argument("--tclr", action='store_true', default=False, help='use TCLR messages from log instead of IMU messages')
parser.add_argument("log", metavar="LOG")
args = parser.parse_args()
import sys
import math
import re
from pymavlink import mavutil
import numpy as np
import matplotlib.pyplot as pyplot
from scipy import signal
from pymavlink.rotmat import Vector3, Matrix3
# fit an order 3 polynomial
POLY_ORDER = 3
# we use a fixed reference temperature of 35C. This has the advantage that
# we don't need to know the final temperature when doing an online calibration
# which allows us to have a calibration timeout
TEMP_REF = 35.0
# we scale the parameters so the values work nicely in
# parameter editors and parameter files that don't
# use exponential notation
SCALE_FACTOR = 1.0e6
AXES = ['X','Y','Z']
AXEST = ['X','Y','Z','T','time']
class Coefficients:
'''class representing a set of coefficients'''
def __init__(self):
self.acoef = {}
self.gcoef = {}
self.enable = [0]*3
self.tmin = [-100]*3
self.tmax = [-100]*3
self.gtcal = {}
self.atcal = {}
self.gofs = {}
self.aofs = {}
def set_accel_poly(self, imu, axis, values):
if imu not in self.acoef:
self.acoef[imu] = {}
self.acoef[imu][axis] = values
def set_gyro_poly(self, imu, axis, values):
if imu not in self.gcoef:
self.gcoef[imu] = {}
self.gcoef[imu][axis] = values
def set_acoeff(self, imu, axis, order, value):
if imu not in self.acoef:
self.acoef[imu] = {}
if axis not in self.acoef[imu]:
self.acoef[imu][axis] = [0]*4
self.acoef[imu][axis][POLY_ORDER-order] = value
def set_gcoeff(self, imu, axis, order, value):
if imu not in self.gcoef:
self.gcoef[imu] = {}
if axis not in self.gcoef[imu]:
self.gcoef[imu][axis] = [0]*4
self.gcoef[imu][axis][POLY_ORDER-order] = value
def set_aoffset(self, imu, axis, value):
if imu not in self.aofs:
self.aofs[imu] = {}
self.aofs[imu][axis] = value
def set_goffset(self, imu, axis, value):
if imu not in self.gofs:
self.gofs[imu] = {}
self.gofs[imu][axis] = value
def set_tmin(self, imu, tmin):
self.tmin[imu] = tmin
def set_tmax(self, imu, tmax):
self.tmax[imu] = tmax
def set_gyro_tcal(self, imu, value):
self.gtcal[imu] = value
def set_accel_tcal(self, imu, value):
self.atcal[imu] = value
def set_enable(self, imu, value):
self.enable[imu] = value
def correction(self, coeff, imu, temperature, axis, cal_temp):
'''calculate correction from temperature calibration from log data using parameters'''
if self.enable[imu] != 1.0:
return 0.0
if cal_temp < -80:
return 0.0
if axis not in coeff:
return 0.0
temperature = constrain(temperature, self.tmin[imu], self.tmax[imu])
cal_temp = constrain(cal_temp, self.tmin[imu], self.tmax[imu])
poly = np.poly1d(coeff[axis])
return poly(cal_temp - TEMP_REF) - poly(temperature - TEMP_REF)
def correction_accel(self, imu, temperature):
'''calculate accel correction from temperature calibration from
log data using parameters'''
cal_temp = self.atcal.get(imu, TEMP_REF)
return Vector3(self.correction(self.acoef[imu], imu, temperature, 'X', cal_temp),
self.correction(self.acoef[imu], imu, temperature, 'Y', cal_temp),
self.correction(self.acoef[imu], imu, temperature, 'Z', cal_temp))
def correction_gyro(self, imu, temperature):
'''calculate gyro correction from temperature calibration from
log data using parameters'''
cal_temp = self.gtcal.get(imu, TEMP_REF)
return Vector3(self.correction(self.gcoef[imu], imu, temperature, 'X', cal_temp),
self.correction(self.gcoef[imu], imu, temperature, 'Y', cal_temp),
self.correction(self.gcoef[imu], imu, temperature, 'Z', cal_temp))
def param_string(self, imu):
params = ''
params += 'INS_TCAL%u_ENABLE 1\n' % (imu+1)
params += 'INS_TCAL%u_TMIN %.1f\n' % (imu+1, self.tmin[imu])
params += 'INS_TCAL%u_TMAX %.1f\n' % (imu+1, self.tmax[imu])
# note that we don't save the first term of the polynomial as that is a
# constant offset which is already handled by the accel/gyro constant
# offsets. We only same the temperature dependent part of the
# calibration
for p in range(POLY_ORDER):
for axis in AXES:
params += 'INS_TCAL%u_ACC%u_%s %.9f\n' % (imu+1, p+1, axis, self.acoef[imu][axis][POLY_ORDER-(p+1)]*SCALE_FACTOR)
for p in range(POLY_ORDER):
for axis in AXES:
params += 'INS_TCAL%u_GYR%u_%s %.9f\n' % (imu+1, p+1, axis, self.gcoef[imu][axis][POLY_ORDER-(p+1)]*SCALE_FACTOR)
return params
class OnlineIMUfit:
'''implement the online learning used in ArduPilot'''
def __init__(self):
pass
def update(self, x, y):
temp = 1.0
for i in range(2*(self.porder - 1), -1, -1):
k = 0 if (i < self.porder) else (i - self.porder + 1)
for j in range(i - k, k-1, -1):
self.mat[j][i-j] += temp
temp *= x
temp = 1.0
for i in range(self.porder-1, -1, -1):
self.vec[i] += y * temp
temp *= x
def get_polynomial(self):
inv_mat = np.linalg.inv(self.mat)
res = np.zeros(self.porder)
for i in range(self.porder):
for j in range(self.porder):
res[i] += inv_mat[i][j] * self.vec[j]
return res
def polyfit(self, x, y, order):
self.porder = order + 1
self.mat = np.zeros((self.porder, self.porder))
self.vec = np.zeros(self.porder)
for i in range(len(x)):
self.update(x[i], y[i])
return self.get_polynomial()
class IMUData:
def __init__(self):
self.accel = {}
self.gyro = {}
def IMUs(self):
'''return list of IMUs'''
if len(self.accel.keys()) != len(self.gyro.keys()):
print("accel and gyro data doesn't match")
sys.exit(1)
return self.accel.keys()
def add_accel(self, imu, temperature, time, value):
if imu not in self.accel:
self.accel[imu] = {}
for axis in AXEST:
self.accel[imu][axis] = np.zeros(0,dtype=float)
self.accel[imu]['T'] = np.append(self.accel[imu]['T'], temperature)
self.accel[imu]['X'] = np.append(self.accel[imu]['X'], value.x)
self.accel[imu]['Y'] = np.append(self.accel[imu]['Y'], value.y)
self.accel[imu]['Z'] = np.append(self.accel[imu]['Z'], value.z)
self.accel[imu]['time'] = np.append(self.accel[imu]['time'], time)
def add_gyro(self, imu, temperature, time, value):
if imu not in self.gyro:
self.gyro[imu] = {}
for axis in AXEST:
self.gyro[imu][axis] = np.zeros(0,dtype=float)
self.gyro[imu]['T'] = np.append(self.gyro[imu]['T'], temperature)
self.gyro[imu]['X'] = np.append(self.gyro[imu]['X'], value.x)
self.gyro[imu]['Y'] = np.append(self.gyro[imu]['Y'], value.y)
self.gyro[imu]['Z'] = np.append(self.gyro[imu]['Z'], value.z)
self.gyro[imu]['time'] = np.append(self.gyro[imu]['time'], time)
def moving_average(self, data, w):
'''apply a moving average filter over a window of width w'''
ret = np.cumsum(data)
ret[w:] = ret[w:] - ret[:-w]
return ret[w - 1:] / w
def FilterArray(self, data, width_s):
'''apply moving average filter of width width_s seconds'''
nseconds = data['time'][-1] - data['time'][0]
nsamples = len(data['time'])
window = int(nsamples / nseconds) * width_s
if window > 1:
for axis in AXEST:
data[axis] = self.moving_average(data[axis], window)
return data
def Filter(self, width_s):
'''apply moving average filter of width width_s seconds'''
for imu in self.IMUs():
self.accel[imu] = self.FilterArray(self.accel[imu], width_s)
self.gyro[imu] = self.FilterArray(self.gyro[imu], width_s)
def accel_at_temp(self, imu, axis, temperature):
'''return the accel value closest to the given temperature'''
if temperature < self.accel[imu]['T'][0]:
return self.accel[imu][axis][0]
for i in range(len(self.accel[imu]['T'])-1):
if temperature >= self.accel[imu]['T'][i] and temperature <= self.accel[imu]['T'][i+1]:
v1 = self.accel[imu][axis][i]
v2 = self.accel[imu][axis][i+1]
p = (temperature - self.accel[imu]['T'][i]) / (self.accel[imu]['T'][i+1]-self.accel[imu]['T'][i])
return v1 + (v2-v1) * p
return self.accel[imu][axis][-1]
def gyro_at_temp(self, imu, axis, temperature):
'''return the gyro value closest to the given temperature'''
if temperature < self.gyro[imu]['T'][0]:
return self.gyro[imu][axis][0]
for i in range(len(self.gyro[imu]['T'])-1):
if temperature >= self.gyro[imu]['T'][i] and temperature <= self.gyro[imu]['T'][i+1]:
v1 = self.gyro[imu][axis][i]
v2 = self.gyro[imu][axis][i+1]
p = (temperature - self.gyro[imu]['T'][i]) / (self.gyro[imu]['T'][i+1]-self.gyro[imu]['T'][i])
return v1 + (v2-v1) * p
return self.gyro[imu][axis][-1]
def constrain(value, minv, maxv):
"""Constrain a value to a range."""
if value < minv:
value = minv
if value > maxv:
value = maxv
return value
def IMUfit(logfile):
'''find IMU calibration parameters from a log file'''
print("Processing log %s" % logfile)
mlog = mavutil.mavlink_connection(logfile)
data = IMUData()
c = Coefficients()
orientation = 0
stop_capture = [ False ] * 3
if args.tclr:
messages = ['PARM','TCLR']
else:
messages = ['PARM','IMU']
while True:
msg = mlog.recv_match(type=messages)
if msg is None:
break
if msg.get_type() == 'PARM':
# build up the old coefficients so we can remove the impact of
# existing coefficients from the data
m = re.match("^INS_TCAL(\d)_ENABLE$", msg.Name)
if m:
imu = int(m.group(1))-1
if stop_capture[imu]:
continue
if msg.Value == 1 and c.enable[imu] == 2:
print("TCAL[%u] enabled" % imu)
stop_capture[imu] = True
continue
if msg.Value == 0 and c.enable[imu] == 1:
print("TCAL[%u] disabled" % imu)
stop_capture[imu] = True
continue
c.set_enable(imu, msg.Value)
m = re.match("^INS_TCAL(\d)_(ACC|GYR)([1-3])_([XYZ])$", msg.Name)
if m:
imu = int(m.group(1))-1
stype = m.group(2)
p = int(m.group(3))
axis = m.group(4)
if stop_capture[imu]:
continue
if stype == 'ACC':
c.set_acoeff(imu, axis, p, msg.Value/SCALE_FACTOR)
if stype == 'GYR':
c.set_gcoeff(imu, axis, p, msg.Value/SCALE_FACTOR)
m = re.match("^INS_TCAL(\d)_TMIN$", msg.Name)
if m:
imu = int(m.group(1))-1
if stop_capture[imu]:
continue
c.set_tmin(imu, msg.Value)
m = re.match("^INS_TCAL(\d)_TMAX", msg.Name)
if m:
imu = int(m.group(1))-1
if stop_capture[imu]:
continue
c.set_tmax(imu, msg.Value)
m = re.match("^INS_GYR(\d)_CALTEMP", msg.Name)
if m:
imu = int(m.group(1))-1
if stop_capture[imu]:
continue
c.set_gyro_tcal(imu, msg.Value)
m = re.match("^INS_ACC(\d)_CALTEMP", msg.Name)
if m:
imu = int(m.group(1))-1
if stop_capture[imu]:
continue
c.set_accel_tcal(imu, msg.Value)
m = re.match("^INS_(ACC|GYR)(\d?)OFFS_([XYZ])$", msg.Name)
if m:
stype = m.group(1)
if m.group(2) == "":
imu = 0
else:
imu = int(m.group(2))-1
axis = m.group(3)
if stop_capture[imu]:
continue
if stype == 'ACC':
c.set_aoffset(imu, axis, msg.Value)
if stype == 'GYR':
c.set_goffset(imu, axis, msg.Value)
if msg.Name == 'AHRS_ORIENTATION':
orientation = int(msg.Value)
print("Using orientation %d" % orientation)
if msg.get_type() == 'TCLR' and args.tclr:
imu = msg.I
T = msg.Temp
if msg.SType == 0:
# accel
acc = Vector3(msg.X, msg.Y, msg.Z)
time = msg.TimeUS*1.0e-6
data.add_accel(imu, T, time, acc)
elif msg.SType == 1:
# gyro
gyr = Vector3(msg.X, msg.Y, msg.Z)
time = msg.TimeUS*1.0e-6
data.add_gyro(imu, T, time, gyr)
if msg.get_type() == 'IMU' and not args.tclr:
imu = msg.I
if stop_capture[imu]:
continue
T = msg.T
acc = Vector3(msg.AccX, msg.AccY, msg.AccZ)
gyr = Vector3(msg.GyrX, msg.GyrY, msg.GyrZ)
# invert the board orientation rotation. Corrections are in sensor frame
if orientation != 0:
acc = acc.rotate_by_inverse_id(orientation)
gyr = gyr.rotate_by_inverse_id(orientation)
if acc is None or gyr is None:
print("Invalid AHRS_ORIENTATION %u" % orientation)
sys.exit(1)
if c.enable[imu] == 1:
acc -= c.correction_accel(imu, T)
gyr -= c.correction_gyro(imu, T)
time = msg.TimeUS*1.0e-6
data.add_accel(imu, T, time, acc)
data.add_gyro (imu, T, time, gyr)
if len(data.IMUs()) == 0:
print("No data found")
sys.exit(1)
print("Loaded %u accel and %u gyro samples" % (len(data.accel[0]['T']),len(data.gyro[0]['T'])))
if not args.tclr:
# apply moving average filter with 2s width
data.Filter(2)
clog = c
c = Coefficients()
calfile = open(args.outfile, "w")
for imu in data.IMUs():
tmin = np.amin(data.accel[imu]['T'])
tmax = np.amax(data.accel[imu]['T'])
c.set_tmin(imu, tmin)
c.set_tmax(imu, tmax)
for axis in AXES:
if args.online:
fit = OnlineIMUfit()
trel = data.accel[imu]['T'] - TEMP_REF
ofs = data.accel_at_temp(imu, axis, clog.atcal[imu])
c.set_accel_poly(imu, axis, fit.polyfit(trel, data.accel[imu][axis] - ofs, POLY_ORDER))
trel = data.gyro[imu]['T'] - TEMP_REF
c.set_gyro_poly(imu, axis, fit.polyfit(trel, data.gyro[imu][axis], POLY_ORDER))
else:
trel = data.accel[imu]['T'] - TEMP_REF
if imu in clog.atcal:
ofs = data.accel_at_temp(imu, axis, clog.atcal[imu])
else:
ofs = np.mean(data.accel[imu][axis])
c.set_accel_poly(imu, axis, np.polyfit(trel, data.accel[imu][axis] - ofs, POLY_ORDER))
trel = data.gyro[imu]['T'] - TEMP_REF
c.set_gyro_poly(imu, axis, np.polyfit(trel, data.gyro[imu][axis], POLY_ORDER))
params = c.param_string(imu)
print(params)
calfile.write(params)
calfile.close()
print("Calibration written to %s" % args.outfile)
if args.no_graph:
return
fig, axs = pyplot.subplots(len(data.IMUs()), 1, sharex=True)
num_imus = len(data.IMUs())
if num_imus == 1:
axs = [axs]
for imu in data.IMUs():
scale = math.degrees(1)
for axis in AXES:
axs[imu].plot(data.gyro[imu]['time'], data.gyro[imu][axis]*scale, label='Uncorrected %s' % axis)
for axis in AXES:
poly = np.poly1d(c.gcoef[imu][axis])
trel = data.gyro[imu]['T'] - TEMP_REF
correction = poly(trel)
axs[imu].plot(data.gyro[imu]['time'], (data.gyro[imu][axis] - correction)*scale, label='Corrected %s' % axis)
if args.log_parm:
for axis in AXES:
if clog.enable[imu] == 0.0:
print("IMU[%u] disabled in log parms" % imu)
continue
poly = np.poly1d(clog.gcoef[imu][axis])
correction = poly(data.gyro[imu]['T'] - TEMP_REF) - poly(clog.gtcal[imu] - TEMP_REF) + clog.gofs[imu][axis]
axs[imu].plot(data.gyro[imu]['time'], (data.gyro[imu][axis] - correction)*scale, label='Corrected %s (log parm)' % axis)
ax2 = axs[imu].twinx()
ax2.plot(data.gyro[imu]['time'], data.gyro[imu]['T'], label='Temperature(C)', color='black')
ax2.legend(loc='upper right')
axs[imu].legend(loc='upper left')
axs[imu].set_title('IMU[%u] Gyro (deg/s)' % imu)
fig, axs = pyplot.subplots(num_imus, 1, sharex=True)
if num_imus == 1:
axs = [axs]
for imu in data.IMUs():
for axis in AXES:
ofs = data.accel_at_temp(imu, axis, clog.atcal.get(imu, TEMP_REF))
axs[imu].plot(data.accel[imu]['time'], data.accel[imu][axis] - ofs, label='Uncorrected %s' % axis)
for axis in AXES:
poly = np.poly1d(c.acoef[imu][axis])
trel = data.accel[imu]['T'] - TEMP_REF
correction = poly(trel)
ofs = data.accel_at_temp(imu, axis, clog.atcal.get(imu, TEMP_REF))
axs[imu].plot(data.accel[imu]['time'], (data.accel[imu][axis] - ofs) - correction, label='Corrected %s' % axis)
if args.log_parm:
for axis in AXES:
if clog.enable[imu] == 0.0:
print("IMU[%u] disabled in log parms" % imu)
continue
poly = np.poly1d(clog.acoef[imu][axis])
ofs = data.accel_at_temp(imu, axis, clog.atcal[imu])
correction = poly(data.accel[imu]['T'] - TEMP_REF) - poly(clog.atcal[imu] - TEMP_REF)
axs[imu].plot(data.accel[imu]['time'], (data.accel[imu][axis] - ofs) - correction, label='Corrected %s (log parm)' % axis)
ax2 = axs[imu].twinx()
ax2.plot(data.accel[imu]['time'], data.accel[imu]['T'], label='Temperature(C)', color='black')
ax2.legend(loc='upper right')
axs[imu].legend(loc='upper left')
axs[imu].set_title('IMU[%u] Accel (m/s^2)' % imu)
pyplot.show()
IMUfit(args.log)