pykinematics.imu.orientation.SSRO¶
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class
pykinematics.imu.orientation.SSRO(c=0.04, N=16, error_factor=1e-15, sigma_g=0.01, sigma_a=0.1, grav=9.81, init_window=8)¶ Sensor-to-Sensor Relative Orientation estimation algorithm.
Parameters: - c : float, optional
Value between 0 and 1 for the cutoff of the estimation of the gravity vector. Default is 0.04.
- N : int, optional
Number of samples in the moving average measurement error estimation. Default is 16.
- error_factor : float, optional
Error factor for the computation of the measurement error for the estimation of the rotation quaternion. Default is 1e-15, recommended is to be slightly less than the values in Q, which can be accomplished with values around dt**2 * sigma_g**2 * 0.1
- sigma_g : float, optional
Gyroscope noise value. Default is 0.01
- sigma_a : float, optional
Accelerometer noise value. Default is 0.1
- grav : float, optional
Gravitational acceleration. Default is 9.81 m/s^2
- init_window : int, optional
Number of samples to use for initialization of state and covariance. Default is 8.
Methods
run(self, s1_a, s2_a, s1_w, s2_w, s1_m, s2_m, dt)Run the SSRO algorithm on the data from two body-segment adjacent sensors. -
run(self, s1_a, s2_a, s1_w, s2_w, s1_m, s2_m, dt)¶ Run the SSRO algorithm on the data from two body-segment adjacent sensors. Rotation quaternion is from the second sensor to the first sensor.
Parameters: - s1_w : numpy.ndarray
Sensor 1 angular velocity in rad/s.
- s2_w : numpy.ndarray
Sensor 2 angular velocity in rad/s.
- s1_a : numpy.ndarray
Sensor 1 acceleration in m/s^2.
- s2_a : numpy.ndarray
Sensor 2 acceleration in m/s^2.
- s1_m : numpy.ndarray
Sensor 1 magnetometer readings.
- s2_m : numpy.ndarray
Sensor 2 magnetometer readings.
- dt : float, optional
Sample rate in seconds.
Returns: - q : numpy.ndarray
Rotation quaternion containing the rotation from sensor 2’s frame to that of sensor 1’s