giant.calibration.estimatorsΒΆ
Modules
Functions
This function estimates a static attitude alignment between one frame and another. |
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This function estimates a temperature dependent attitude alignment between one frame and another. |
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This function takes a fit temperature dependent alignment solution and evaluates what the alignment rotation is at a specified temperature. |
Classes
GeometricEstimatorOptions(weighted_estimation: bool = False, a_priori_model_covariance: Optional[numpy.ndarray[tuple[Any, ...], numpy.dtype[numpy.float64]]] = None) |
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This protocol class serves as the template for implementing a class for doing geometric camera model estimation in GIANT. |
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IterativeNonlinearLstSqOptions(weighted_estimation: bool = False, a_priori_model_covariance: Optional[numpy.ndarray[tuple[Any, ...], numpy.dtype[numpy.float64]]] = None, max_iter: int = 20, residual_atol: float = 1e-10, residual_rtol: float = 1e-10, state_atol: float = 1e-10, state_rtol: float = 1e-10) |
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This concrete estimator implements iterative non-linear least squares for estimating an updated camera model. |
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LMAEstimatorOptions(weighted_estimation: bool = False, a_priori_model_covariance: Optional[numpy.ndarray[tuple[Any, ...], numpy.dtype[numpy.float64]]] = None, max_iter: int = 20, residual_atol: float = 1e-10, residual_rtol: float = 1e-10, state_atol: float = 1e-10, state_rtol: float = 1e-10, max_divergence_steps: int = 5) |
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This implements a Levenberg-Marquardt Algorithm estimator, which is analogous to a damped iterative non-linear least squares. |
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Named tuple to make the results clear |