Struct EKFLocalizer 

pub struct EKFLocalizer { /* private fields */ }

Extended Kalman Filter for robot localization

Implementations

impl EKFLocalizer

pub fn new(config: EKFConfig) -> Self

Create a new EKF localizer

pub fn try_new(config: EKFConfig) -> RoboticsResult<Self>

Create a new validated EKF localizer

pub fn with_defaults() -> Self

Create with default configuration

pub fn with_initial_state(initial_state: EKFState, config: EKFConfig) -> Self

Create with initial state

pub fn try_with_initial_state( initial_state: EKFState, config: EKFConfig, ) -> RoboticsResult<Self>

Create with validated initial state

pub fn with_initial_state_2d( initial_state: State2D, config: EKFConfig, ) -> RoboticsResult<Self>

Create with common State2D type

pub fn get_covariance_matrix(&self) -> &Matrix4<f64>

Get reference to state covariance

pub fn state_2d(&self) -> State2D

Get current estimate as State2D

pub fn set_process_noise(&mut self, q: Matrix4<f64>)

Set process noise covariance

pub fn try_set_process_noise(&mut self, q: Matrix4<f64>) -> RoboticsResult<()>

Set process noise covariance with validation

pub fn set_measurement_noise(&mut self, r: Matrix2<f64>)

Set measurement noise covariance

pub fn try_set_measurement_noise( &mut self, r: Matrix2<f64>, ) -> RoboticsResult<()>

Set measurement noise covariance with validation

pub fn estimate( &mut self, measurement: &EKFMeasurement, control: &EKFControl, dt: f64, ) -> Result<&EKFState, RoboticsError>

Full EKF estimation step (predict + update)

pub fn estimate_state( &mut self, measurement: Point2D, control: ControlInput, dt: f64, ) -> RoboticsResult<State2D>

EKF estimate step using common crate types

pub fn ekf_estimation( x_est: EKFState, p_est: Matrix4<f64>, z: EKFMeasurement, u: EKFControl, q: Matrix4<f64>, r: Matrix2<f64>, dt: f64, ) -> (EKFState, Matrix4<f64>)

Legacy interface for EKF estimation (standalone function style)

Trait Implementations

impl StateEstimator for EKFLocalizer

type State = Matrix<f64, Const<4>, Const<1>, ArrayStorage<f64, 4, 1>>

State type used by this estimator

type Measurement = Matrix<f64, Const<2>, Const<1>, ArrayStorage<f64, 2, 1>>

Measurement type used by this estimator

type Control = Matrix<f64, Const<2>, Const<1>, ArrayStorage<f64, 2, 1>>

Control input type

fn predict(&mut self, control: &Self::Control, dt: f64)

Prediction step

fn update(&mut self, measurement: &Self::Measurement)

Update step with measurement

fn get_state(&self) -> &Self::State

Get current state estimate

fn get_covariance(&self) -> Option<&DMatrix<f64>>

Get current covariance estimate (if applicable)