def run_example(typ_filter="martin"): """ run example : "mahony" or "martin" """ # Compute (True) or load (False [params_acc, params_mag, params_gyr] = calib_param(compute=False) # Load the recording data [time_imu, accx, accy, accz, mx, my, mz, gyrx, gyry, gyrz] = \ load_foxcsvfile(DATAFILE) acc_imu = np.column_stack([accx, accy, accz]) mag_imu = np.column_stack([mx, my, mz]) gyr_imu = np.column_stack([gyrx, gyry, gyrz]) # Init output quat = np.zeros((len(acc_imu), 4)) euler = np.zeros((len(acc_imu), 3)) observer = martin_ahrs.martin_ahrs() # Computation loop for i in range(0, len(acc_imu)): # Applies the Scale and Offset to data acc_imu[i, :] = normalize_data(acc_imu[i, :], params_acc) mag_imu[i, :] = normalize_data(mag_imu[i, :], params_mag) gyr_imu[i, :] = normalize_data(gyr_imu[i, :], params_gyr) # Filter call if i == 0: if typ_filter == "mahony": quat[0, :] = [1, 0, 0, 0] else: quat[0] = observer.init_observer( np.hstack([acc_imu[0, :], mag_imu[0, :], gyr_imu[0, :]])) else: if typ_filter == "mahony": quat[i] = mahony.update( quat[i - 1], np.hstack([acc_imu[i, :], mag_imu[i, :], gyr_imu[i, :]])) euler[i] = quat2euler(quat[i]) else: quat[i] = observer.update( np.hstack([acc_imu[i, :], mag_imu[i, :], gyr_imu[i, :]]), 0.005) euler[i] = quat2euler(quat[i]) #Plot results plot_quat(typ_filter, time_imu,\ quat[:,0], quat[:,1], quat[:,2], quat[:,3]) if typ_filter == "mahony": plot_euler(typ_filter, time_imu,\ euler[:,2], euler[:,1], euler[:,0]) else: plot_euler(typ_filter, time_imu,\ euler[:,2], euler[:,1], euler[:,0])
def update(self, data): [self.quaternion] = np.array(madgwick.update(self.quaternion, data)) self.euler = np.array(quat2euler(self.quaternion)) print 'Quaternion : ' + str(self.quaternion) +'\n' + 'Rz : '+'%0.2f' %((self.euler[0])*180/pi)+' '+u'°' +\ ' Ry : '+'%0.2f' %((self.euler[1])*180/pi)+' '+u'°' + ' Rx : '+'%0.2f' %((self.euler[2])*180/pi)+' '+u'°' # Rotation along z self.rot_z_display.text = 'Z Rotation : ' + '%0.2f' % ( (self.euler[0]) * 180 / pi) + ' ' + u'°' # Rotation along y self.rot_y_display.text = 'Y Rotation : ' + '%0.2f' % ( (self.euler[1]) * 180 / pi) + ' ' + u'°' # Rotation along x self.rot_x_display.text = 'X Rotation : ' + '%0.2f' % ( (self.euler[2]) * 180 / pi) + ' ' + u'°' # Put the IMU in its original position self.IMU.axis = (8, 0, 0) self.IMU.up = (1, 0, 0) # Rotate it of arccos(qw)*2 on the axis qx,qy,qz self.IMU.rotate(origin=(0, 0, 0), angle=np.arccos(self.quaternion[0]) * 2, axis=(self.quaternion[1:4]))
def update(self, data): self.quaternion = nq.mult(self.quat_offset, self.observer.update(data[0, 2:12], 0.005)) self.euler = np.array(quat2euler(self.quaternion)) print 'Quaternion : ' + str(self.quaternion) +'\n' + 'Rz : '+'%0.2f' %((self.euler[0])*180/pi)+' '+u'°' +\ ' Ry : '+'%0.2f' %((self.euler[1])*180/pi)+' '+u'°' + ' Rx : '+'%0.2f' %((self.euler[2])*180/pi)+' '+u'°' # Rotation along z self.rot_z_display.text = 'Z Rotation : ' + '%0.2f' % ( (self.euler[0]) * 180 / pi) + ' ' + u'°' # Rotation along y self.rot_y_display.text = 'Y Rotation : ' + '%0.2f' % ( (self.euler[1]) * 180 / pi) + ' ' + u'°' # Rotation along x self.rot_x_display.text = 'X Rotation : ' + '%0.2f' % ( (self.euler[2]) * 180 / pi) + ' ' + u'°' # Put the IMU in its original position self.IMU.axis = (8, 0, 0) self.IMU.up = (1, 0, 0) # Rotate it of arccos(qw)*2 on the axis qx,qy,qz if self.quaternion[0] >= -1 and self.quaternion[0] <= 1: self.IMU.rotate(origin=(0, 0, 0), angle=np.arccos(self.quaternion[0]) * 2, axis=(self.quaternion[1:4]))
def test_quats(): for x, y, z in eg_rots: M1 = nea.euler2mat(z, y, x) quatM = nq.mat2quat(M1) quat = nea.euler2quat(z, y, x) yield nq.nearly_equivalent, quatM, quat quatS = sympy_euler2quat(z, y, x) yield nq.nearly_equivalent, quat, quatS zp, yp, xp = nea.quat2euler(quat) # The parameters may not be the same as input, but they give the # same rotation matrix M2 = nea.euler2mat(zp, yp, xp) yield assert_array_almost_equal, M1, M2
def run(): """ run example """ # Load data [_, _, mag_calib, _] = load_data(data_calib_file) # Compute calib params [offset_mag, scale_mag] = calib_mag_param(mag_calib) params_mag = np.vstack((offset_mag, scale_mag)) # Load data [time_imu, acc_imu, mag_imu, gyr_imu] = load_data(data_file) # Init output quat = np.zeros((len(acc_imu), 4)) quat_corr = np.zeros((len(acc_imu), 4)) euler = np.zeros((len(acc_imu), 3)) observer = martin_ahrs.martin_ahrs() data_init = np.mean( np.hstack([acc_imu[0:100, :], mag_imu[0:100, :], gyr_imu[0:100, :]]), 0) # mean of the first static 2 seconds quat[0] = observer.init_observer(data_init) # Computation loop for i in range(0, len(acc_imu)): # Applies the Scale and Offset to data mag_imu[i, :] = normalize_data(mag_imu[i, :], params_mag) # Filter call quat[i] = observer.update( np.hstack([acc_imu[i, :], mag_imu[i, :], gyr_imu[i, :]]), 0.005) conj_q_init = nq.conjugate(quat[100, :]) for i in range(0, len(acc_imu) - 1): quat_corr[i + 1] = nq.mult(conj_q_init, quat[i + 1]) euler[i] = quat2euler(quat_corr[i + 1]) #load vicon data head_angle_vicon = np.loadtxt('data/3/head_angle_vicon.txt') time_delay = 0.3 #Plot results plt.hold(True) plt.plot(np.arange(0, len(head_angle_vicon) / 100., 0.01), head_angle_vicon) plt.plot(time_imu - time_delay, euler[:, 0] * 180 / np.pi - 6, 'k--', linewidth='1.5') plt.legend(('head_angle_vicon', 'head_angle_imu')) plt.xlabel('Time (s)') plt.ylabel('Angle (deg)')
def tug(): trial_number = 8 calib_sensor_shank_right = 'data/CALIB/9_IMU4_RIGHT_SHANK.csv' #rot calib_sensor_thigh_right = 'data/CALIB/9_IMU6_RIGHT_THIGH.csv' #ref calib_sensor_shank_left = 'data/CALIB/9_IMU5_LEFT_SHANK.csv' #rot calib_sensor_thigh_left = 'data/CALIB/9_IMU9_LEFT_THIGH.csv' #ref calib_sensor_trunk = 'data/CALIB/9_IMU8_TRUNK.csv' trial_file_shank_right = 'data/' + str(trial_number) + '/' + str( trial_number) + '_IMU4_RIGHT_SHANK.csv' trial_file_thigh_right = 'data/' + str(trial_number) + '/' + str( trial_number) + '_IMU6_RIGHT_THIGH.csv' trial_file_shank_left = 'data/' + str(trial_number) + '/' + str( trial_number) + '_IMU5_LEFT_SHANK.csv' trial_file_thigh_left = 'data/' + str(trial_number) + '/' + str( trial_number) + '_IMU9_LEFT_THIGH.csv' trial_file_trunk = 'data/' + str(trial_number) + '/' + str( trial_number) + '_IMU8_TRUNK.csv' ###################################################### # Sensors (scale/offset) calib parameters computation ###################################################### [_, params_mag_right_thigh, _] = \ calib.compute(imuNumber=6 ,filepath=calib_sensor_thigh_right, param = 3) [_, params_mag_right_shank, _] = \ calib.compute(imuNumber=4 ,filepath=calib_sensor_shank_right, param = 3) [_, params_mag_left_thigh, _] = \ calib.compute(imuNumber=9 ,filepath=calib_sensor_thigh_left, param = 3) [_, params_mag_left_shank, _] = \ calib.compute(imuNumber=5 ,filepath=calib_sensor_shank_left, param = 3) [_, params_mag_trunk, _] = \ calib.compute(imuNumber=8 ,filepath=calib_sensor_trunk, param = 3) scale_mag_right_thigh = params_mag_right_thigh[1:4, :] bias_mag_right_thigh = params_mag_right_thigh[0, :] scale_mag_right_shank = params_mag_right_shank[1:4, :] bias_mag_right_shank = params_mag_right_shank[0, :] scale_mag_left_thigh = params_mag_left_thigh[1:4, :] bias_mag_left_thigh = params_mag_left_thigh[0, :] scale_mag_left_shank = params_mag_left_shank[1:4, :] bias_mag_left_shank = params_mag_left_shank[0, :] scale_mag_trunk = params_mag_left_shank[1:4, :] bias_mag_trunk = params_mag_left_shank[0, :] ###################################################### # Load trials data ###################################################### # right thigh [time_imu_right_thigh, accx_right_thigh, accy_right_thigh, accz_right_thigh, \ mx_right_thigh, my_right_thigh, mz_right_thigh, gyrx_right_thigh, gyry_right_thigh, gyrz_right_thigh] = \ load_foxcsvfile(trial_file_thigh_right) # left thigh [time_imu_left_thigh, accx_left_thigh, accy_left_thigh, accz_left_thigh, \ mx_left_thigh, my_left_thigh, mz_left_thigh, gyrx_left_thigh, gyry_left_thigh, gyrz_left_thigh] = \ load_foxcsvfile(trial_file_thigh_left) # right shank [time_imu_right_shank, accx_right_shank, accy_right_shank, accz_right_shank, \ mx_right_shank, my_right_shank, mz_right_shank, gyrx_right_shank, gyry_right_shank, gyrz_right_shank] = \ load_foxcsvfile(trial_file_shank_right) # left shank [time_imu_left_shank, accx_left_shank, accy_left_shank, accz_left_shank, \ mx_left_shank, my_left_shank, mz_left_shank, gyrx_left_shank, gyry_left_shank, gyrz_left_shank] = \ load_foxcsvfile(trial_file_shank_left) # trunk [time_imu_trunk, accx_trunk, accy_trunk, accz_trunk, \ mx_trunk, my_trunk, mz_trunk, gyrx_trunk, gyry_trunk, gyrz_trunk] = \ load_foxcsvfile(trial_file_trunk) ###################################################### # Applies scale and offset on magnetometer data ###################################################### nb_common_samples = np.amin([len(time_imu_right_thigh), len(time_imu_left_thigh), len(time_imu_right_shank),\ len(time_imu_left_shank), len(time_imu_trunk)]) acc_imu_right_thigh = np.column_stack([ accx_right_thigh[0:nb_common_samples], accy_right_thigh[0:nb_common_samples], accz_right_thigh[0:nb_common_samples] ]) mag_imu_right_thigh = np.column_stack([ mx_right_thigh[0:nb_common_samples], my_right_thigh[0:nb_common_samples], mz_right_thigh[0:nb_common_samples] ]) gyr_imu_right_thigh = np.column_stack([ gyrx_right_thigh[0:nb_common_samples], gyry_right_thigh[0:nb_common_samples], gyrz_right_thigh[0:nb_common_samples] ]) acc_imu_left_thigh = np.column_stack([ accx_left_thigh[0:nb_common_samples], accy_left_thigh[0:nb_common_samples], accz_left_thigh[0:nb_common_samples] ]) mag_imu_left_thigh = np.column_stack([ mx_left_thigh[0:nb_common_samples], my_left_thigh[0:nb_common_samples], mz_left_thigh[0:nb_common_samples] ]) gyr_imu_left_thigh = np.column_stack([ gyrx_left_thigh[0:nb_common_samples], gyry_left_thigh[0:nb_common_samples], gyrz_left_thigh[0:nb_common_samples] ]) acc_imu_right_shank = np.column_stack([ accx_right_shank[0:nb_common_samples], accy_right_shank[0:nb_common_samples], accz_right_shank[0:nb_common_samples] ]) mag_imu_right_shank = np.column_stack([ mx_right_shank[0:nb_common_samples], my_right_shank[0:nb_common_samples], mz_right_shank[0:nb_common_samples] ]) gyr_imu_right_shank = np.column_stack([ gyrx_right_shank[0:nb_common_samples], gyry_right_shank[0:nb_common_samples], gyrz_right_shank[0:nb_common_samples] ]) acc_imu_left_shank = np.column_stack([ accx_left_shank[0:nb_common_samples], accy_left_shank[0:nb_common_samples], accz_left_shank[0:nb_common_samples] ]) mag_imu_left_shank = np.column_stack([ mx_left_shank[0:nb_common_samples], my_left_shank[0:nb_common_samples], mz_left_shank[0:nb_common_samples] ]) gyr_imu_left_shank = np.column_stack([ gyrx_left_shank[0:nb_common_samples], gyry_left_shank[0:nb_common_samples], gyrz_left_shank[0:nb_common_samples] ]) acc_imu_trunk = np.column_stack([ accx_trunk[0:nb_common_samples], accy_trunk[0:nb_common_samples], accz_trunk[0:nb_common_samples] ]) mag_imu_trunk = np.column_stack([ mx_trunk[0:nb_common_samples], my_trunk[0:nb_common_samples], mz_trunk[0:nb_common_samples] ]) gyr_imu_trunk = np.column_stack([ gyrx_trunk[0:nb_common_samples], gyry_trunk[0:nb_common_samples], gyrz_trunk[0:nb_common_samples] ]) # Applies scale and offset for i in range(0, nb_common_samples): mag_imu_right_thigh[i, :] = np.transpose( np.dot( scale_mag_right_thigh, np.transpose((mag_imu_right_thigh[i, :] - np.transpose(bias_mag_right_thigh))))) mag_imu_left_thigh[i, :] = np.transpose( np.dot( scale_mag_left_thigh, np.transpose((mag_imu_left_thigh[i, :] - np.transpose(bias_mag_left_thigh))))) mag_imu_right_shank[i, :] = np.transpose( np.dot( scale_mag_right_shank, np.transpose((mag_imu_right_shank[i, :] - np.transpose(bias_mag_right_shank))))) mag_imu_left_shank[i, :] = np.transpose( np.dot( scale_mag_left_shank, np.transpose((mag_imu_left_shank[i, :] - np.transpose(bias_mag_left_shank))))) mag_imu_trunk[i, :] = np.transpose( np.dot( scale_mag_trunk, np.transpose( (mag_imu_trunk[i, :] - np.transpose(bias_mag_trunk))))) ########################################### # Geometrical calib (q_offset computation) ########################################### data_static_right_thigh = np.hstack( (acc_imu_right_thigh[0:1200], mag_imu_right_thigh[0:1200], gyr_imu_right_thigh[0:1200])) data_static_left_thigh = np.hstack( (acc_imu_left_thigh[0:1200], mag_imu_left_thigh[0:1200], gyr_imu_left_thigh[0:1200])) data_static_right_shank = np.hstack( (acc_imu_right_shank[0:1200], mag_imu_right_shank[0:1200], gyr_imu_right_shank[0:1200])) data_static_left_shank = np.hstack( (acc_imu_left_shank[0:1200], mag_imu_left_shank[0:1200], gyr_imu_left_shank[0:1200])) data_static_trunk = np.hstack( (acc_imu_trunk[0:1200], mag_imu_trunk[0:1200], gyr_imu_trunk[0:1200])) q_offset_right = \ calib_geom.compute(data_static_right_thigh, data_static_right_shank) q_offset_left = \ calib_geom.compute(data_static_left_thigh, data_static_left_shank) ################################ # Quaternions computation # ################################ # Observers initiations observer_right_thigh = martin.martin_ahrs() observer_left_thigh = martin.martin_ahrs() observer_right_shank = martin.martin_ahrs() observer_left_shank = martin.martin_ahrs() observer_trunk = martin.martin_ahrs() # euler_ref = np.zeros((len(acc_imu_ref),3)) # Quaternions initiation q_right_thigh = np.zeros((nb_common_samples, 4)) q_right_shank = np.zeros((nb_common_samples, 4)) q_left_thigh = np.zeros((nb_common_samples, 4)) q_left_shank = np.zeros((nb_common_samples, 4)) q_trunk = np.zeros((nb_common_samples, 4)) # Init data data_init_right_thigh = np.mean(data_static_right_thigh[:, 0:10], 0) data_init_left_thigh = np.mean(data_static_left_thigh[:, 0:10], 0) data_init_right_shank = np.mean(data_static_right_shank[:, 0:10], 0) data_init_left_shank = np.mean(data_static_left_shank[:, 0:10], 0) data_init_trunk = np.mean(data_static_trunk[:, 0:10], 0) q_right_thigh[0, :] = observer_right_thigh.init_observer( data_init_right_thigh ) #build the observer from the mean values of the geom calib position q_left_thigh[0, :] = observer_left_thigh.init_observer( data_init_left_thigh ) #build the observer from the mean values of the geom calib position q_right_shank[0, :] = observer_right_shank.init_observer( data_init_right_shank ) #build the observer from the mean values of the geom calib position q_left_shank[0, :] = observer_left_shank.init_observer( data_init_left_shank ) #build the observer from the mean values of the geom calib position q_trunk[0, :] = observer_trunk.init_observer( data_init_trunk ) #build the observer from the mean values of the geom calib position for i in range(0, nb_common_samples - 1): q_right_thigh[i + 1] = observer_right_thigh.update( np.hstack([ acc_imu_right_thigh[i, :], mag_imu_right_thigh[i, :], gyr_imu_right_thigh[i, :] ]), 0.005) q_left_thigh[i + 1] = observer_left_thigh.update( np.hstack([ acc_imu_left_thigh[i, :], mag_imu_left_thigh[i, :], gyr_imu_left_thigh[i, :] ]), 0.005) q_right_shank[i + 1] = observer_right_shank.update( np.hstack([ acc_imu_right_shank[i, :], mag_imu_right_shank[i, :], gyr_imu_right_shank[i, :] ]), 0.005) q_left_shank[i + 1] = observer_left_shank.update( np.hstack([ acc_imu_left_shank[i, :], mag_imu_left_shank[i, :], gyr_imu_left_shank[i, :] ]), 0.005) q_trunk[i + 1] = observer_trunk.update( np.hstack([ acc_imu_trunk[i, :], mag_imu_trunk[i, :], gyr_imu_trunk[i, :] ]), 0.005) ########################################################################### # Compute angles from the quaternions ########################################################################### knee_angle_right = np.zeros((nb_common_samples, 1)) knee_angle_left = np.zeros((nb_common_samples, 1)) for i in range(0, nb_common_samples - 1): [knee_angle_left[i], _] = \ goniometer.compute(q_left_thigh[i], q_left_shank[i], q_offset_left.reshape(4,)) [knee_angle_right[i], _] = \ goniometer.compute(q_right_thigh[i], q_right_shank[i], q_offset_right.reshape(4,)) # Frame change from NEDown to initial frame conj_q_init_trunk = nq.conjugate(q_trunk[1200, :]) euler_trunk = np.zeros((nb_common_samples, 3)) for i in range(0, nb_common_samples - 1): q_trunk[i] = nq.mult(conj_q_init_trunk, q_trunk[i]) euler_trunk[i] = quat2euler(q_trunk[i]) # Plots # plt.figure() # plt.hold(True) # plt.plot(euler_trunk[:,2]*180/pi) # plt.plot(knee_angle_right*180/pi) # plt.plot(knee_angle_left*180/pi) # Save the calculated quaternions to a .mat file quat_dict = {} quat_dict['knee_angle_right'] = knee_angle_right quat_dict['knee_angle_left'] = knee_angle_left quat_dict['quaternion_trunk'] = q_trunk quat_dict['euler_trunk_ZYX'] = euler_trunk sio.savemat('export_' + str(trial_number) + '.mat', quat_dict)
def run_example(): """ run example : "martin" """ # Compute (True) or load (False [params_acc, params_mag, params_gyr] = calib_param(compute=False) # Load the recording data [time_sens, accx, accy, accz, mx, my, mz, gyrx, gyry, gyrz] = \ load_foxcsvfile(DATAFILE) # Find motionless begin periods freqs = 200 start, end = find_static_periods(gyrz, 2 * np.pi / 180, 3 * freqs) static_duration = time_sens[end[0]] - time_sens[start[0]] print "LGHT", start[0], len(end) if static_duration < 5.0: print "Warning: static duration too low" time_imu = time_sens acc_imu = np.column_stack([accx, accy, accz]) mag_imu = np.column_stack([mx, my, mz]) gyr_imu = np.column_stack([gyrx, gyry, gyrz]) # Init output quat = np.zeros((len(acc_imu), 4)) euler = np.zeros((len(acc_imu), 3)) observer = martin_ahrs.martin_ahrs() quat_offset = [1, 0, 0, 0] # Initialization loop for i in range(0, end[0]): # Applies the Scale and Offset to data acc_imu[i, :] = normalize_data(acc_imu[i, :], params_acc) mag_imu[i, :] = normalize_data(mag_imu[i, :], params_mag) gyr_imu[i, :] = normalize_data(gyr_imu[i, :], params_gyr) # Filter call if i == 0: quat[0] = observer.init_observer( np.hstack([acc_imu[0, :], mag_imu[0, :], gyr_imu[0, :]])) else: quat[i] = observer.update( np.hstack([acc_imu[i, :], mag_imu[i, :], gyr_imu[i, :]]), 0.005) quat_offset = nq.conjugate(quat[end - 1][0]) print "Quaternion init", quat_offset # Computation loop for i in range(end[0], len(acc_imu)): # Applies the Scale and Offset to data acc_imu[i, :] = normalize_data(acc_imu[i, :], params_acc) mag_imu[i, :] = normalize_data(mag_imu[i, :], params_mag) gyr_imu[i, :] = normalize_data(gyr_imu[i, :], params_gyr) # Filter call quat[i] = observer.update( np.hstack([acc_imu[i, :], mag_imu[i, :], gyr_imu[i, :]]), 0.005) quat[i] = nq.mult(quat_offset, quat[i]) euler[i] = quat2euler(quat[i]) # Plot results plot_quat("Expe Prima ", time_imu,\ quat[:,0], quat[:,1], quat[:,2], quat[:,3]) plot_euler("Expe Prima ", time_imu,\ euler[:,2], euler[:,1], euler[:,0]) # Save results save_ahrs_csvfile(ANGLEFILE, time_imu, quat, euler)
def VPython_quaternion_3D(): # Compute # [params_acc, params_mag, params_gyr] = \ # calib.compute(imuNumber=6 ,filepath="data/CALIB.csv", param = 3) [params_acc, params_mag, params_gyr] = \ calib.load_param("data/CalibrationFileIMU6.txt") # Load the recording data [time_imu, accx, accy, accz, mx, my, mz, gyrx, gyry, gyrz] = \ load_foxcsvfile("data/1_IMU6_RIGHT_FOOT.csv") # Applies the Scale and Offset to data scale_acc = params_acc[1:4, :] bias_acc = params_acc[0, :] scale_mag = params_mag[1:4, :] bias_mag = params_mag[0, :] scale_gyr = params_gyr[1:4, :] bias_gyr = params_gyr[0, :] acc_imu = np.column_stack([accx, accy, accz]) mag_imu = np.column_stack([mx, my, mz]) gyr_imu = np.column_stack([gyrx, gyry, gyrz]) quaternion = np.zeros((len(acc_imu), 4)) euler = np.zeros((len(acc_imu), 3)) quaternion[0, :] = [1, 0, 0, 0] for i in range(0, len(acc_imu) - 1): acc_imu[i, :] = np.transpose( np.dot(scale_acc, np.transpose((acc_imu[i, :] - np.transpose(bias_acc))))) mag_imu[i, :] = np.transpose( np.dot(scale_mag, np.transpose((mag_imu[i, :] - np.transpose(bias_mag))))) gyr_imu[i, :] = np.transpose( np.dot(scale_gyr, np.transpose((gyr_imu[i, :] - np.transpose(bias_gyr))))) quaternion[i + 1] = madgwick.update( quaternion[i], np.hstack([acc_imu[i, :], mag_imu[i, :], gyr_imu[i, :]])) euler[i] = quat2euler(quaternion[i + 1]) ####################### # Plots ####################### dataVicon = np.loadtxt('data/RIGHT_FOOT.txt', skiprows=4) time_vicon = dataVicon[:, 0] euler_vicon = np.zeros((len(time_vicon), 3)) quat_offset = np.hstack( [dataVicon[0, 7], dataVicon[0, 4], dataVicon[0, 5], dataVicon[0, 6]]) for i in range(1, len(time_vicon) - 1): quat_vicon = np.hstack([ dataVicon[i, 7], dataVicon[i, 4], dataVicon[i, 5], dataVicon[i, 6] ]) euler_vicon[i] = quat2euler( nq.mult(nq.conjugate(quat_offset), quat_vicon)) #Z plt.figure() plt.hold(True) plt.plot(time_imu - (0.2), euler[:, 0] * 180 / pi) plt.plot(time_vicon, euler_vicon[:, 0] * 180 / pi) plt.legend(('z_imu', 'z_vicon')) xlabel('time (s)') ylabel('angle (deg)') #Y plt.figure() plt.hold(True) plt.plot(time_imu - (0.2), euler[:, 1] * 180 / pi) plt.plot(time_vicon, euler_vicon[:, 1] * 180 / pi) plt.legend(('y_imu', 'y_vicon')) xlabel('time (s)') ylabel('angle (deg)') plt.show() #X plt.figure() plt.hold(True) plt.plot(time_imu - (0.2), euler[:, 2] * 180 / pi) plt.plot(time_vicon, euler_vicon[:, 2] * 180 / pi) plt.legend(('x_imu', 'x_vicon')) xlabel('time (s)') ylabel('angle (deg)') plt.show() quat_dict = {} quat_dict['quaternion'] = quaternion sio.savemat('quaternion_madgwick.mat', quat_dict)
def compute_attitude(datafile, calibfile, anglefile, plotting=True): """ compute attitude IMU sensor """ # Load calibration parameters [params_acc, params_mag, params_gyr] = calib.load_param(calibfile) # Load the recording data [time_sens, accx, accy, accz, magx, magy, magz, gyrx, gyry, gyrz] = \ load_foxcsvfile(datafile) # Find motionless begin periods freqs = 200 start, end = find_static_periods(gyrz, 2 * np.pi / 180, 3 * freqs) static_duration = time_sens[end[0]] - time_sens[start[0]] if static_duration < 5.0: print "Warning: static duration too low" time_imu = time_sens acc_imu = np.column_stack([accx, accy, accz]) mag_imu = np.column_stack([magx, magy, magz]) gyr_imu = np.column_stack([gyrx, gyry, gyrz]) # Init output quat = np.zeros((len(acc_imu), 4)) euler = np.zeros((len(acc_imu), 3)) observer = martin_ahrs.martin_ahrs() # Initialization loop quat_offset = [1, 0, 0, 0] for i in range(0, end[0]): # Applies the Scale and Offset to data acc_imu[i, :] = normalize_data(acc_imu[i, :], params_acc) mag_imu[i, :] = normalize_data(mag_imu[i, :], params_mag) gyr_imu[i, :] = normalize_data(gyr_imu[i, :], params_gyr) # Filter call if i == 0: quat[0] = observer.init_observer( np.hstack([acc_imu[0, :], mag_imu[0, :], gyr_imu[0, :]])) else: quat[i] = observer.update( np.hstack([acc_imu[i, :], mag_imu[i, :], gyr_imu[i, :]]), 0.005) quat_offset = nq.conjugate(quat[end - 1][0]) print "Quaternion init", quat_offset # Computation loop for i in range(end[0], len(acc_imu)): # Applies the Scale and Offset to data acc_imu[i, :] = normalize_data(acc_imu[i, :], params_acc) mag_imu[i, :] = normalize_data(mag_imu[i, :], params_mag) gyr_imu[i, :] = normalize_data(gyr_imu[i, :], params_gyr) # Filter call quat[i] = observer.update( np.hstack([acc_imu[i, :], mag_imu[i, :], gyr_imu[i, :]]), 0.005) quat[i] = nq.mult(quat_offset, quat[i]) euler[i] = quat2euler(quat[i]) # Plot results if plotting is True: plot_quat("Expe Prima ", time_imu, quat[:, 0], quat[:, 1], quat[:, 2], quat[:, 3]) plot_euler("Expe Prima ", time_imu, euler[:, 2], euler[:, 1], euler[:, 0]) # Save results save_ahrs_csvfile(anglefile, time_imu, quat, euler)
def VPython_quaternion_3D(): # Compute ## Example 1 ## [params_acc, params_mag, params_gyr] = \ ## calib.compute(imuNumber=6 ,filepath="data/CALIB.csv", param = 3) # # [params_acc, params_mag, params_gyr] = \ # calib.load_param("data/CalibrationFileIMU6.txt") # # Load the recording data # [time_imu, accx, accy, accz, mx, my, mz, gyrx, gyry, gyrz] = \ # load_foxcsvfile("data/1_IMU6_RIGHT_FOOT.csv") # dataVicon = np.loadtxt('data/RIGHT_FOOT.txt',skiprows=4) # Example 2 [params_acc, params_mag, params_gyr] = \ calib.compute(imuNumber=5 ,filepath="data2/CALIB.csv", param = 3) # # [params_acc, params_mag, params_gyr] = \ # calib.load_param("data2/CalibrationFileIMU5.txt") # Load the recording data [time_imu, accx, accy, accz, mx, my, mz, gyrx, gyry, gyrz] = \ load_foxcsvfile("data2/5_IMU5_LEFT_FOOT.csv") dataVicon = np.loadtxt('data2/LEFT_FOOT.txt', skiprows=4) time_delay = 3.05 ## Example 3 # [params_acc, params_mag, params_gyr] = \ # calib.compute(imuNumber=5 ,filepath="data3/CALIB.csv", param = 3) ### ## [params_acc, params_mag, params_gyr] = \ ## calib.load_param("data2/CalibrationFileIMU5.txt") ## ## # Load the recording data # [time_imu, accx, accy, accz, mx, my, mz, gyrx, gyry, gyrz] = \ # load_foxcsvfile("data3/4_IMU5_LEFT_FOOT.csv") ## # dataVicon = np.loadtxt('data3/LEFT_FOOT.txt',skiprows=4) # time_delay = 0.35 # Applies the Scale and Offset to data scale_mag = params_mag[1:4, :] bias_mag = params_mag[0, :] scale_gyr = params_gyr[1:4, :] bias_gyr = params_gyr[0, :] acc_imu = np.column_stack([accx, accy, accz]) mag_imu = np.column_stack([mx, my, mz]) gyr_imu = np.column_stack([gyrx, gyry, gyrz]) observer = martin.martin_ahrs() quaternion = np.zeros((len(acc_imu), 4)) quat_imu_corr = np.zeros((len(acc_imu), 4)) euler = np.zeros((len(acc_imu), 3)) data_init = np.mean( np.hstack([acc_imu[0:200, :], mag_imu[0:200, :], gyr_imu[0:200, :]]), 0) # mean of the first static 2 seconds quaternion[0, :] = observer.init_observer(data_init) for i in range(1, len(acc_imu) - 1): mag_imu[i, :] = np.transpose( np.dot(scale_mag, np.transpose((mag_imu[i, :] - np.transpose(bias_mag))))) gyr_imu[i, :] = np.transpose( np.dot(scale_gyr, np.transpose((gyr_imu[i, :] - np.transpose(bias_gyr))))) quaternion[i + 1] = observer.update( np.hstack([acc_imu[i, :], mag_imu[i, :], gyr_imu[i, :]]), 0.005) conj_q_init = nq.conjugate(quaternion[150, :]) for i in range(1, len(acc_imu) - 1): quat_imu_corr[i + 1] = nq.mult(conj_q_init, quaternion[i + 1]) euler[i] = quat2euler(quat_imu_corr[i + 1]) ####################### # Plots ####################### time_vicon = dataVicon[:, 0] euler_vicon = np.zeros((len(time_vicon), 3)) quat_vicon_corr = np.zeros((len(time_vicon), 4)) quat_offset = np.hstack( [dataVicon[0, 7], dataVicon[0, 4], dataVicon[0, 5], dataVicon[0, 6]]) for i in range(1, len(time_vicon) - 1): quat_vicon = np.hstack([ dataVicon[i, 7], dataVicon[i, 4], dataVicon[i, 5], dataVicon[i, 6] ]) quat_vicon_corr[i] = nq.mult(nq.conjugate(quat_offset), quat_vicon) euler_vicon[i] = quat2euler(quat_vicon_corr[i]) #Z plt.figure() plt.hold(True) plt.plot(time_imu - time_delay, euler[:, 0] * 180 / pi) plt.plot(time_vicon, euler_vicon[:, 0] * 180 / pi) plt.legend(('z_imu', 'z_vicon')) xlabel('time (s)') ylabel('angle (deg)') #Y plt.figure() plt.hold(True) plt.plot(time_imu - time_delay, euler[:, 1] * 180 / pi) plt.plot(time_vicon, euler_vicon[:, 1] * 180 / pi) plt.legend(('y_imu', 'y_vicon')) xlabel('time (s)') ylabel('angle (deg)') plt.show() #X plt.figure() plt.hold(True) plt.plot(time_imu - time_delay, euler[:, 2] * 180 / pi) plt.plot(time_vicon, euler_vicon[:, 2] * 180 / pi) plt.legend(('x_imu', 'x_vicon')) xlabel('time (s)') ylabel('angle (deg)') plt.show() # Save the calculated quaternions to a .mat file quat_dict = {} quat_dict['quat_imu_corr'] = quat_imu_corr quat_dict['euler_imu_corr_ZYX'] = euler quat_dict['quat_vicon_corr'] = quat_vicon_corr quat_dict['euler_vicon_corr_ZYX'] = euler_vicon quat_dict['time_imu_corr'] = time_imu - time_delay quat_dict['time_vicon_corr'] = time_vicon sio.savemat('export.mat', quat_dict)
def goniometer_example(): calib_sensor_shank = 'data/CALIB_SENSORS/1_IMU23_SHANK.csv' #rot calib_sensor_thigh = 'data/CALIB_SENSORS/1_IMU22_THIGH.csv' #ref calib_geom_shank = 'data/CALIB_GEOM/2_IMU23_SHANK.csv' calib_geom_thigh = 'data/CALIB_GEOM/2_IMU22_THIGH.csv' trial_file_shank = 'data/STAND_TO_SIT_TO_STAND_TO_SIT/3_IMU23_SHANK.csv' trial_file_thigh = 'data/STAND_TO_SIT_TO_STAND_TO_SIT/3_IMU22_THIGH.csv' ###################################################### # Sensors (scale/offset) calib parameters computation ###################################################### [params_acc_ref, params_mag_ref, params_gyr_ref] = \ calib.compute(imuNumber=22 ,filepath=calib_sensor_thigh, param = 3) [params_acc_rot, params_mag_rot, params_gyr_rot] = \ calib.compute(imuNumber=23 ,filepath=calib_sensor_shank, param = 3) scale_acc_ref = params_acc_ref[1:4,:] bias_acc_ref = params_acc_ref[0,:] scale_mag_ref = params_mag_ref[1:4,:] bias_mag_ref = params_mag_ref[0,:] scale_gyr_ref = params_gyr_ref[1:4,:] bias_gyr_ref = params_gyr_ref[0,:] scale_acc_rot = params_acc_rot[1:4,:] bias_acc_rot = params_acc_rot[0,:] scale_mag_rot = params_mag_rot[1:4,:] bias_mag_rot = params_mag_rot[0,:] scale_gyr_rot = params_gyr_rot[1:4,:] bias_gyr_rot = params_gyr_rot[0,:] ########################################### # Geometrical calib (q_offset computation) ########################################### # load data from the recording calib [time_imu_calib_ref, accx_calib_ref, accy_calib_ref, accz_calib_ref, mx_calib_ref,\ my_calib_ref, mz_calib_ref, gyrx_calib_ref, gyry_calib_ref, gyrz_calib_ref] = \ load_foxcsvfile(calib_geom_thigh) [time_imu_calib_rot, accx_calib_rot, accy_calib_rot, accz_calib_rot, mx_calib_rot,\ my_calib_rot, mz_calib_rot, gyrx_calib_rot, gyry_calib_rot, gyrz_calib_rot] = \ load_foxcsvfile(calib_geom_shank) ############################ acc_imu_calib_ref = np.column_stack([accx_calib_ref, accy_calib_ref, accz_calib_ref]) mag_imu_calib_ref = np.column_stack([mx_calib_ref, my_calib_ref, mz_calib_ref]) gyr_imu_calib_ref = np.column_stack([gyrx_calib_ref, gyry_calib_ref, gyrz_calib_ref]) # Applies scale and offset on raw calib trial data (ref) for i in range(0,len(acc_imu_calib_ref)-1): acc_imu_calib_ref[i,:]= np.transpose(np.dot(scale_acc_ref,np.transpose((acc_imu_calib_ref[i,:]-np.transpose(bias_acc_ref))))) mag_imu_calib_ref[i,:]= np.transpose(np.dot(scale_mag_ref,np.transpose((mag_imu_calib_ref[i,:]-np.transpose(bias_mag_ref))))) gyr_imu_calib_ref[i,:]= np.transpose(np.dot(scale_gyr_ref,np.transpose((gyr_imu_calib_ref[i,:]-np.transpose(bias_gyr_ref))))) ############################ acc_imu_calib_rot = np.column_stack([accx_calib_rot, accy_calib_rot, accz_calib_rot]) mag_imu_calib_rot = np.column_stack([mx_calib_rot, my_calib_rot, mz_calib_rot]) gyr_imu_calib_rot = np.column_stack([gyrx_calib_rot, gyry_calib_rot, gyrz_calib_rot]) # Applies scale and offset on raw calib trial data (rot) for i in range(0,len(acc_imu_calib_rot)-1): acc_imu_calib_rot[i,:]= np.transpose(np.dot(scale_acc_rot,np.transpose((acc_imu_calib_rot[i,:]-np.transpose(bias_acc_rot))))) mag_imu_calib_rot[i,:]= np.transpose(np.dot(scale_mag_rot,np.transpose((mag_imu_calib_rot[i,:]-np.transpose(bias_mag_rot))))) gyr_imu_calib_rot[i,:]= np.transpose(np.dot(scale_gyr_rot,np.transpose((gyr_imu_calib_rot[i,:]-np.transpose(bias_gyr_rot))))) ############################ data_ref_calib_geom = np.hstack((acc_imu_calib_ref, mag_imu_calib_ref, gyr_imu_calib_ref)) data_rot_calib_geom = np.hstack((acc_imu_calib_rot, mag_imu_calib_rot, gyr_imu_calib_rot)) q_offset = \ calib_geom.compute(data_ref_calib_geom, data_rot_calib_geom) ####################### # Trial data loading ####################### # Loads trial data (ref imu thigh) [time_imu_ref, accx_ref, accy_ref, accz_ref, mx_ref, my_ref, mz_ref, gyrx_ref, gyry_ref, gyrz_ref] = \ load_foxcsvfile(trial_file_thigh) # Loads trial data (rot imu shank) [time_imu_rot, accx_rot, accy_rot, accz_rot, mx_rot, my_rot, mz_rot, gyrx_rot, gyry_rot, gyrz_rot] = \ load_foxcsvfile(trial_file_shank) ################################ # q_ref quaternion computation # ################################ # Applies the Scale and Offset to data ref acc_imu_ref = np.column_stack([accx_ref, accy_ref, accz_ref]) mag_imu_ref = np.column_stack([mx_ref, my_ref, mz_ref]) gyr_imu_ref = np.column_stack([gyrx_ref, gyry_ref, gyrz_ref]) # Quaternions calculation q_ref (thigh) observer_ref = martin.martin_ahrs() euler_ref = np.zeros((len(acc_imu_ref),3)) q_ref = np.zeros((len(acc_imu_ref),4)) data_ref_imu0 = np.mean(data_ref_calib_geom[:,0:10],0) data_ref_imu1 = np.mean(data_rot_calib_geom[:,0:10],0) #q_ref[0,:] = observer_ref.init_observer(np.hstack([acc_imu_ref[0,:],mag_imu_ref[0,:], gyr_imu_ref[0,:]])) q_ref[0,:] = observer_ref.init_observer(data_ref_imu0) #build the observer from the mean values of the geom calib position for i in range(1,len(acc_imu_ref)-1): # acc_imu_ref[i,:]= np.transpose(np.dot(scale_acc_ref,np.transpose((acc_imu_ref[i,:]-np.transpose(bias_acc_ref))))) mag_imu_ref[i,:]= np.transpose(np.dot(scale_mag_ref,np.transpose((mag_imu_ref[i,:]-np.transpose(bias_mag_ref))))) gyr_imu_ref[i,:]= np.transpose(np.dot(scale_gyr_ref,np.transpose((gyr_imu_ref[i,:]-np.transpose(bias_gyr_ref))))) q_ref[i+1]= observer_ref.update(np.hstack([acc_imu_ref[i,:],mag_imu_ref[i,:], gyr_imu_ref[i,:]]), 0.005) euler_ref[i]=quat2euler(q_ref[i+1]) ################################ # q_rot quaternion computation # ################################ # Applies the Scale and Offset to data rot acc_imu_rot = np.column_stack([accx_rot, accy_rot, accz_rot]) mag_imu_rot = np.column_stack([mx_rot, my_rot, mz_rot]) gyr_imu_rot = np.column_stack([gyrx_rot, gyry_rot, gyrz_rot]) # Quaternions calculation q_rot (shank) observer_rot = martin.martin_ahrs() euler_rot = np.zeros((len(acc_imu_rot),3)) q_rot = np.zeros((len(acc_imu_rot),4)) #q_rot[0,:] = observer_rot.init_observer(np.hstack([acc_imu_rot[0,:],mag_imu_rot[0,:], gyr_imu_rot[0,:]])) q_rot[0,:] = observer_rot.init_observer(data_ref_imu1) #build the observer from the mean values of the geom calib position for i in range(0,len(acc_imu_rot)-1): # acc_imu_rot[i,:]= np.transpose(np.dot(scale_acc_rot,np.transpose((acc_imu_rot[i,:]-np.transpose(bias_acc_rot))))) mag_imu_rot[i,:]= np.transpose(np.dot(scale_mag_rot,np.transpose((mag_imu_rot[i,:]-np.transpose(bias_mag_rot))))) gyr_imu_rot[i,:]= np.transpose(np.dot(scale_gyr_rot,np.transpose((gyr_imu_rot[i,:]-np.transpose(bias_gyr_rot))))) q_rot[i+1]= observer_rot.update(np.hstack([acc_imu_rot[i,:],mag_imu_rot[i,:], gyr_imu_rot[i,:]]), 0.005) euler_rot[i]= quat2euler(q_rot[i+1]) ########################################################################### # Compute angles from the two quaternions angle = np.zeros((len(q_ref)-1)) rot_axis = np.zeros((len(q_ref),3)) euler = np.zeros((len(q_ref),3)) for i in range(0,len(q_ref)-1): [angle[i], rot_axis[i]] = \ goniometer.compute(q_ref[i], q_rot[i], q_offset.reshape(4,)) euler[i]=quat2euler(np.hstack((angle[i],rot_axis[i]))) f, axis = plt.subplots(2, sharex=True) # Plot angle axis[0].plot(angle*180/np.pi, label='angle') axis[0].set_title('Angle') axis[0].set_ylabel('deg') axis[0].legend() # Plot rot axis axis[1].plot(rot_axis) axis[1].set_title('Rot. Axis') axis[1].legend(('x','y','z'))