navlie.utils.common.MixtureResultList¶

class navlie.utils.common.MixtureResultList(result_list: List[MixtureResult])¶

Bases: GaussianResultList

Parameters:: result_list (List[GaussianResult]) – A list of GaussianResult, intended such that each element corresponds to a different time point

Let N = len(result_list)

model_probabilities¶

static from_estimates(estimate_list: List[MixtureState], state_true_list: List[State], method='nearest')¶

A convenience function that creates a MixtureResultList from a list of MixtureState and a list of true State objects

Parameters:

estimate_list (List[MixtureState]) – A list of MixtureState objects
state_true_list (List[State]) – A list of true State objects
method ("nearest" or "linear", optional) – The method used to interpolate the true state when the timestamps do not line up exactly, by default “nearest”.

Returns:

A MixtureResultList object

Return type:

MixtureResultList

stamp¶

timestamp

Type:: numpy.ndarray with shape (N,)

state: List[State]¶

numpy array of State objects

Type:: numpy.ndarray with shape (N,)

state_true: List[State]¶

numpy array of true State objects

Type:: numpy.ndarray with shape (N,)

covariance: ndarray¶

covariance

Type:: numpy.ndarray with shape (N,dof,dof)

error¶

error throughout trajectory

Type:: numpy.ndarray with shape (N, dof)

ees¶

EES throughout trajectory

Type:: numpy.ndarray with shape (N,)

nees¶

NEES throughout trajectory

Type:: numpy.ndarray with shape (N,)

md¶

Mahalanobis distance throughout trajectory

Type:: numpy.ndarray with shape (N,)

three_sigma¶

three-sigma bounds

Type:: numpy.ndarray with shape (N, dof)

value¶

state value. type depends on implementation

Type:: numpy.ndarray with shape (N,)

value_true¶

true state value. type depends on implementation

Type:: numpy.ndarray with shape (N,)

dof¶

dof throughout trajectory

Type:: numpy.ndarray with shape (N,)

nees_lower_bound(confidence_interval: float)¶

Calculates the NEES lower bound throughout the trajectory.

Parameters:: confidence_interval (float) – Single-sided cumulative probability threshold that defines the bound. Must be between 0 and 1
Returns:: NEES value corresponding to confidence interval
Return type:: numpy.ndarray with shape (N,)

An example of how to make a NEES plot with both upper and lower bounds:

ax.plot(results.stamp, results.nees)
ax.plot(results.stamp, results.nees_lower_bound(0.99))
ax.plot(results.stamp, results.nees_upper_bound(0.99))

nees_upper_bound(confidence_interval: float, double_sided=True)¶

Calculates the NEES upper bound throughout the trajectory

Parameters:

confidence_interval (float) – Cumulative probability threshold that defines the bound. Must be between 0 and 1.
double_sided (bool, optional) – Whether the provided threshold is single-sided or double sided, by default True

Returns:

numpy.ndarray with shape (N,) – NEES value corresponding to confidence interval
An example of how to make a NEES plot with only upper bounds
.. code-block:: python – ax.plot(results.stamp, results.nees) ax.plot(results.stamp, results.nees_upper_bound(0.99, double_sided=False))

rmse¶

EES throughout trajectory

Type:: numpy.ndarray with shape (N,)

navlie.utils.common.MixtureResult

navlie.utils.common.MonteCarloResult