navlie.lib.models.RangePointToAnchor¶

class navlie.lib.models.RangePointToAnchor(anchor_position: List[float], R: float)¶

Bases: MeasurementModel

Range measurement from a point state to an anchor (which is also another point). I.e., the state is a vector with the first dim elements being the position of the point. This model is of the form

\[y = ||\mathbf{r}_a - \mathbf{r}||\]

where \(\mathbf{r}_a\) is the anchor position and \(\mathbf{r}\) is the point position.

Parameters:

anchor_position (np.ndarray or List[float]) – Position of anchor.
R (float) – Variance of measurement noise.

evaluate(x: VectorState) → ndarray¶: Evaluates the measurement model \(\mathbf{g}(\mathcal{X})\).

jacobian(x: VectorState) → ndarray¶: Evaluates the measurement model Jacobian with respect to the state.

\[\mathbf{G} = \frac{D \mathbf{g}(\mathcal{X})}{D \mathcal{X}}\]

covariance(x: VectorState) → ndarray¶: Returns the covariance \(\mathbf{R}\) associated with additive Gaussian noise.

evaluate_with_jacobian(x: State) → Tuple[ndarray, ndarray]¶: Evaluates the measurement model and simultaneously returns the Jacobian as its second output argument. This is useful to override for performance reasons when the model evaluation and Jacobian have a lot of common calculations, and it is more efficient to calculate them in the same function call.

jacobian_fd(x: State, step_size=1e-06)¶: Calculates the model jacobian with finite difference.

sqrt_information(x: State)¶

navlie.lib.models.PointRelativePositionSLAM

navlie.lib.models.RangePoseToAnchor