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