Submodules¶

sensbiotk.calib.calib module¶

Calib for IMU sensors data

sensbiotk.calib.calib.calib_imu_parameters(acc, mag, gyr)[source]¶

Compute IMU calibration parameters

Returns:	params : numpy array of float [ [off_accx off_accy off_accz], offset for accelerometers [sca_accx sca_accy sca_accz], scale for accelerometers [off_magx off_magy off_magz], offset for magnetometers [sca_magx sca_magy sca_magz], scale for magnetometers [off_gyrx off_gyry off_gyrz], offset for gyrometers [sca_gyrx sca_gyry sca_gyrz]] scale for gyrometers

sensbiotk.calib.calib.compute(imuNumber, filepath=None, param=1)[source]¶

Performs IMU calibration parameters, saves them in a file at the same path than the .csv recording used, and returns them.

Returns:	None :

sensbiotk.calib.calib.load_param(filename)[source]¶

Load IMU calibration parameters

Returns:	params : numpy array of float [ [off_accx off_accy off_accz], offset for accelerometers [sca_accx sca_accy sca_accz], scale for accelerometers [off_magx off_magy off_magz], offset for magnetometers [sca_magx sca_magy sca_magz], scale for magnetometers [off_gyrx off_gyry off_gyrz], offset for gyrometers [sca_gyrx sca_gyry sca_gyrz]] scale for gyrometers

sensbiotk.calib.calib.print_param(params_acc, params_mag, params_gyr)[source]¶

Print IMU calibration parameters

Returns:	None :

sensbiotk.calib.calib.save_param(filename, pcalib_acc, pcalib_mag, pcalib_gyr, comments='')[source]¶

Save IMU calibration parameters

Returns:	None :

sensbiotk.calib.calib.test1()[source]¶

Test1

sensbiotk.calib.calib.test2()[source]¶

Test2

sensbiotk.calib.calib.test3()[source]¶

Test3

sensbiotk.calib.calib_acc module¶

Calib algo for accelerometers sensors data

sensbiotk.calib.calib_acc.compute(data)[source]¶

Computes the Scale factor Matrix and the bias vector of a MEMS accelerometer. The procedure exploits the fact that, in static conditions, the modulus of the accelerometer output vector matches that of the gravity acceleration. The calibration model incorporates the bias and scale factor for each axis and the cross-axis symmetrical actors. The parameters are computed through Gauss-Newton nonlinear optimization.

The mathematical model used is A = M(V - B) where M (scale) and B (bias) are scale factor matrix and bias vector respectively.

M = [ Mxx Mxy Mxz; Myx Myy Myz; Mzx Mzy Mzz ];

where Mxy = Myx; Myz = Mzy; Mxz = Mzx;

B = [ Bx; By; Bz ];

The diagonal elements of M represent the scale factors along the three axes, whereas the other elements of M are called cross-axis factors. These terms allow describing both the axes misalignment and the crosstalk effect between different channels caused by the sensor electronics. In an ideal world, M = 1; B = 0

Parameters:	data : np.array (x,3) columns ax, ay and az acceleration values obtained from : the accelerometer represented in m.s-2 : x rows represent averaged data from x different static positions
Returns:	scale : np.array the scale factor matrix biais : np.array the bias vector (in m.s-2)

sensbiotk.calib.calib_acc.compute_simple(data)[source]¶

Compute IMU accelerometer calibration parameters

Returns:	offset : numpy array of float (dim=3) offset parameter scale : numpy array of float (dim=3) scale parameters

sensbiotk.calib.calib_acc.errorf(v_x, v_y, v_z, mxx, mxy, mxz, myy, myz, mzz, b_x, b_y, b_z)[source]¶

error function given by Ax**2+Ay**2+Az**2 - g**2, where g = 9.81

sensbiotk.calib.calib_acc.jac1(v_x, v_y, v_z, mxx, mxy, mxz, b_x, b_y, b_z)[source]¶

Functions jac1 to jac9 are the elements of the Jacobian vector (partial derivatives of the error function with respect to the gain and bias components)

sensbiotk.calib.calib_acc.jac2(v_x, v_y, v_z, mxx, mxy, mxz, myy, myz, b_x, b_y, b_z)[source]¶

element 2 of the jacobian

sensbiotk.calib.calib_acc.jac3(v_x, v_y, v_z, mxx, mxy, mxz, myz, mzz, b_x, b_y, b_z)[source]¶

element 3 of the jacobian

sensbiotk.calib.calib_acc.jac4(v_x, v_y, v_z, mxy, myy, myz, b_x, b_y, b_z)[source]¶

element 4 of the jacobian

sensbiotk.calib.calib_acc.jac5(v_x, v_y, v_z, mxy, mxz, myy, myz, mzz, b_x, b_y, b_z)[source]¶

element 5 of the jacobian

sensbiotk.calib.calib_acc.jac6(v_x, v_y, v_z, mxz, myz, mzz, b_x, b_y, b_z)[source]¶

element 6 of the jacobian

sensbiotk.calib.calib_acc.jac7(v_x, v_y, v_z, mxx, mxy, mxz, myy, myz, mzz, b_x, b_y, b_z)[source]¶

element 7 of the jacobian

sensbiotk.calib.calib_acc.jac8(v_x, v_y, v_z, mxx, mxy, mxz, myy, myz, mzz, b_x, b_y, b_z)[source]¶

element 8 of the jacobian

sensbiotk.calib.calib_acc.jac9(v_x, v_y, v_z, mxx, mxy, mxz, myy, myz, mzz, b_x, b_y, b_z)[source]¶

f9 function

sensbiotk.calib.calib_acc.scale_fit(data, constant)[source]¶

Compute scale from a constant signal to fit with a constant

Returns:	offset :numpy array of float (dim=3) : scale parameters

sensbiotk.calib.calib_acc.scale_fit_norm(data, constant)[source]¶

Compute scale from a constant signal to fit with a constant

Returns:	offset :float (dim=1) : scale parameters

Notes

To allow a good fitting, The motion must cover all axis rotation without high acceleration.

sensbiotk.calib.calib_geom module¶

Created on Thu Dec 11 11:25:46 2014

@author: bsijober

Calib for IMU sensors data

sensbiotk.calib.calib_geom.compute(data_imu0, data_imu1)[source]¶

Compute an “offset” quaternion between two supposed aligned quaternions

Returns:	q_offset : quaternion [w, x, y, z] the offset quaternion between IMU0 and IMU1

sensbiotk.calib.calib_gyr module¶

Calib algo for gyrometer sensors data

sensbiotk.calib.calib_gyr.compute(data)[source]¶

Compute IMU gyrometer calibration parameters

Returns:	scale : numpy array of float (dim=3) scale parameters offset : numpy array of float (dim=3) offset parameter

sensbiotk.calib.calib_gyr.offset_fit(data)[source]¶

Compute offset from a constant signal to fit with zero

Returns:	offset :numpy array of float (dim=3) : offset parameters

Notes

To allow a good fitting, it must be with no motion

sensbiotk.calib.calib_mag module¶

Calib algo for magnetometers sensors data

sensbiotk.calib.calib_mag.compute(data)[source]¶

Compute IMU magnetometer calibration parameters

Returns:	scale : numpy array of float (dim=3) scale parameters offset : numpy array of float (dim=3) offset parameter

sensbiotk.calib.calib_mag.unit_sphere_fit(data)[source]¶

Calibration using Unit Sphere Fitting algo.

Returns:	scale :numpy array of float : scale parameters offset :numpy array of float : offset parameters

Notes

To allow a good fitting, the motion must cover all axis rotation without high acceleration.

Module contents¶