giant.calibration.estimators.TemperatureDependentAlignmentEstimator

class giant.calibration.estimators.TemperatureDependentAlignmentEstimator(frame_1_rotations=None, frame_2_rotations=None, temperatures=None, order='xyz')

This class estimates a temperature dependent attitude alignment between one frame and another.

The temperature dependent alignment is found by fitting linear temperature dependent euler angles (or Tait-Bryan angles) to transform from the first frame to the second. That is

$${\mathbf{T}}_B={\mathbf{R}}_m({\theta }_m(t)){\mathbf{R}}_n({\theta }_n(t)){\mathbf{R}}_p({\theta }_p(t)){\mathbf{T}}_A$$

where ${\mathbf{T}}_B$ is the target frame, ${\mathbf{R}}_i$ is the rotation matrix about the $i^{th}$ axis, ${\mathbf{T}}_A$ is the base frame, and ${\theta }_i(t)$ are the linear angles.

This fit is done in a least squares sense by computing the values for ${\theta }_i(t)$ across a range of temperatures (by estimating the attitude for multiple single images) and then solving the system

$$\begin{array}{r}\left[\begin{array}{cc}1& t_1\\ 1& t_2\\ ⋮& ⋮\\ 1& t_n\end{array}\right]\left[\begin{array}{ccc}{\theta }_{m0}& {\theta }_{n0}& {\theta }_{p0}\\ {\theta }_{m1}& {\theta }_{n1}& {\theta }_{p1}\end{array}\right]=\left[\begin{array}{ccc}{\phantom{\theta }}^0{\theta }_m& {\phantom{\theta }}^0{\theta }_n& {\phantom{\theta }}^0{\theta }_p\\ ⋮& ⋮& ⋮\\ {\phantom{\theta }}^k{\theta }_m& {\phantom{\theta }}^k{\theta }_n& {\phantom{\theta }}^k{\theta }_p\end{array}\right]\end{array}$$

where ${\phantom{\theta }}^k{\theta }_i$ is the measured Euler/Tait-Bryan angle for the $k^{th}$ image.

In general a user should not use this class and instead the Calibration.estimate_temperature_dependent_alignment() should be used which handles the proper setup.

Parameters:

• frame_1_rotations (Iterable[Rotation] | None) – The rotation objects from the inertial frame to the base frame

• frame_2_rotations (Iterable[Rotation] | None) – The rotation objects from the inertial frame to the target frame

• temperatures (List[Real] | None) – The temperature of the camera corresponding to the times the input rotations were estimated.

• order (str) – The order of the rotations to perform according to the convention in quaternion_to_euler()

frame_1_rotations: Iterable[Rotation] | None

An iterable containing the rotations from the inertial frame to the base frame for each image under consideration.

frame_2_rotations: Iterable[Rotation] | None

An iterable containing the rotations from the inertial frame to the target frame for each image under consideration.

temperatures: List[Real] | None

A list containing the temperatures of the camera for each image under consideration

order: str

The order of the Euler angles according to the convention in quaternion_to_euler()

angle_m_offset: float | None

The estimated constant angle offset for the m rotation axis in radians.

This will be None until estimate() is called.

angle_m_slope: float | None

The estimated angle temperature slope for the m rotation axis in radians.

This will be None until estimate() is called.

angle_n_offset: float | None

The estimated constant angle offset for the n rotation axis in radians.

This will be None until estimate() is called.

angle_n_slope: float | None

The estimated angle temperature slope for the n rotation axis in radians.

This will be None until estimate() is called.

angle_p_offset: float | None

The estimated constant angle offset for the p rotation axis in radians.

This will be None until estimate() is called.

angle_p_slope: float | None

The estimated angle temperature slope for the p rotation axis in radians.

This will be None until estimate() is called.

Summary of Methods

estimate

This method estimates the linear temperature dependent alignment as 3 linear temperature dependent euler angles according to order.