IMUs¶
These routines facilitate the calculation of 3d movement kinematics for recordings from inertial measurement units (IMUs).
Functions¶
General¶
imus.data_import()
... Read in Rate and stored 3D parameters from XSens IMUs
IMUs¶
imus.calc_QPos()
... Calculate orientation and position, from angular velocity and linear accelerationimus.kalman_quat()
... Calculate orientation from IMU-data using an Extended Kalman Filter
Class¶
imus.IMU ([inFile, inData]) |
Class for working with working with inertial measurement units (IMUs) |
Methods¶
imus.IMU.calc_orientation (R_initialOrientation) |
Calculate the current orientation |
imus.IMU.calc_position (initialPosition) |
Calculate the position, assuming that the orientation is already known. |
imus.IMU.setData (data) |
Set the properties of an IMU-object. |
Class¶
imus.MadgwickAHRS ([SamplePeriod, Beta, ...]) |
Class¶
imus.MahonyAHRS ([SamplePeriod, Kp, Ki, ...]) |
Madgwick’s implementation of Mayhony’s AHRS algorithm |
Details¶
Utilities for movements recordins with inertial measurement units (IMUs)
-
class
imus.
IMU
(inFile=None, inData=None)[source]¶ Class for working with working with inertial measurement units (IMUs)
- An IMU object can be initialized
- by giving a filename
- by providing measurement data and a samplerate
- without giving any parameter; in that case the user is prompted to select an infile
Parameters: - inFile (string) – path- and file-name of infile, if you get the sound from a file.
- inData (dictionary) –
The following fields are required:
- acc : (N x 3) array
- Linear acceleration [m/s^2], in the x-, y-, and z-direction
- omega : (N x 3) array
- Angular velocity [deg/s], about the x-, y-, and z-axis
- [mag] : (N x 3) array (optional)
- Local magnetic field, in the x-, y-, and z-direction
- rate: integer
- sample rate [Hz]
Notes
- IMU-Properties:
- source
- acc
- omega
- mag
- rate
- totalSamples
- duration
- dataType
Examples
>>> myimu = IMU(r'.\Data\Walking_02.txt') >>> >>> initialOrientation = np.array([[1,0,0], >>> [0,0,-1], >>> [0,1,0]]) >>> initialPosition = np.r_[0,0,0] >>> >>> myimu.calc_orientation(initialOrientation) >>> q_simple = myimu.quat[:,1:] >>> >>> calcType = 'Madgwick' >>> myimu.calc_orientation(initialOrientation, type=calcType) >>> q_Kalman = myimu.quat[:,1:]
-
calc_orientation
(R_initialOrientation, type='quatInt')[source]¶ Calculate the current orientation
Parameters: - R_initialOrientation (3x3 array) – approximate alignment of sensor-CS with space-fixed CS
- type (string) –
- ‘quatInt’ ...... quaternion integration of angular velocity
- ‘Kalman’ ..... quaternion Kalman filter
- ‘Madgwick’ .. gradient descent method, efficient
- ‘Mahony’ ... Formula from Mahony, as implemented by Madgwick
-
class
imus.
MahonyAHRS
(SamplePeriod=0.00390625, Kp=1.0, Ki=0, Quaternion=[1, 0, 0, 0])[source]¶ Madgwick’s implementation of Mayhony’s AHRS algorithm
-
imus.
calc_QPos
(R_initialOrientation, omega, initialPosition, accMeasured, rate)[source]¶ Reconstruct position and orientation, from angular velocity and linear acceleration. Assumes a start in a stationary position. No compensation for drift.
Parameters: - omega (ndarray(N,3)) – Angular velocity, in [rad/s]
- accMeasured (ndarray(N,3)) – Linear acceleration, in [m/s^2]
- initialPosition (ndarray(3,)) – initial Position, in [m]
- R_initialOrientation (ndarray(3,3)) – Rotation matrix describing the initial orientation of the sensor, except a mis-orienation with respect to gravity
- rate (float) – sampling rate, in [Hz]
Returns: - q (ndarray(N,3)) – Orientation, expressed as a quaternion vector
- pos (ndarray(N,3)) – Position in space [m]
Example
>>> q1, pos1 = calc_QPos(R_initialOrientation, omega, initialPosition, acc, rate)
-
imus.
import_data
(inFile, type='XSens', paramList=[])[source]¶ Read in Rate and stored 3D parameters from XSens IMUs
Parameters: - inFile (string) – Path and name of input file
- paramList (list of strings) – Starting names of stored parameters
Returns: - List, containing
- rate (float) – Sampling rate
- [List of x/y/z Parameter Values]
Examples
>>> data = getXSensData(fullInFile, ['Acc', 'Gyr']) >>> rate = data[0] >>> accValues = data[1] >>> Omega = data[2]
-
imus.
kalman_quat
(rate, acc, gyr, mag)[source]¶ Calclulate the orientation from IMU magnetometer data.
Parameters: - rate (float) – sample rate [Hz]
- acc ((N,3) ndarray) – linear acceleration [m/sec^2]
- gyr ((N,3) ndarray) – angular velocity [rad/sec]
- mag ((N,3) ndarray) – magnetic field orientation
Returns: qOut – unit quaternion, describing the orientation relativ to the coordinate system spanned by the local magnetic field, and gravity
Return type: (N,4) ndarray
Notes
Based on “Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking” Yun, X. and Bachman, E.R., IEEE TRANSACTIONS ON ROBOTICS, VOL. 22, 1216-1227 (2006)