Trait opencv::hub_prelude::KalmanFilterTrait

pub trait KalmanFilterTrait: KalmanFilterTraitConst {
    fn as_raw_mut_KalmanFilter(&mut self) -> *mut c_void;

    fn set_state_pre(&mut self, val: Mat) { ... }
    fn set_state_post(&mut self, val: Mat) { ... }
    fn set_transition_matrix(&mut self, val: Mat) { ... }
    fn set_control_matrix(&mut self, val: Mat) { ... }
    fn set_measurement_matrix(&mut self, val: Mat) { ... }
    fn set_process_noise_cov(&mut self, val: Mat) { ... }
    fn set_measurement_noise_cov(&mut self, val: Mat) { ... }
    fn set_error_cov_pre(&mut self, val: Mat) { ... }
    fn set_gain(&mut self, val: Mat) { ... }
    fn set_error_cov_post(&mut self, val: Mat) { ... }
    fn set_temp1(&mut self, val: Mat) { ... }
    fn set_temp2(&mut self, val: Mat) { ... }
    fn set_temp3(&mut self, val: Mat) { ... }
    fn set_temp4(&mut self, val: Mat) { ... }
    fn set_temp5(&mut self, val: Mat) { ... }
    fn init(
        &mut self,
        dynam_params: i32,
        measure_params: i32,
        control_params: i32,
        typ: i32
    ) -> Result<()> { ... }
    fn predict(&mut self, control: &Mat) -> Result<Mat> { ... }
    fn correct(&mut self, measurement: &Mat) -> Result<Mat> { ... }
}

Required Methods

fn as_raw_mut_KalmanFilter(&mut self) -> *mut c_void

Provided Methods

fn set_state_pre(&mut self, val: Mat)

predicted state (x’(k)): x(k)=Ax(k-1)+Bu(k)

fn set_state_post(&mut self, val: Mat)

corrected state (x(k)): x(k)=x’(k)+K(k)(z(k)-Hx’(k))

fn set_transition_matrix(&mut self, val: Mat)

state transition matrix (A)

fn set_control_matrix(&mut self, val: Mat)

control matrix (B) (not used if there is no control)

fn set_measurement_matrix(&mut self, val: Mat)

measurement matrix (H)

fn set_process_noise_cov(&mut self, val: Mat)

process noise covariance matrix (Q)

fn set_measurement_noise_cov(&mut self, val: Mat)

measurement noise covariance matrix (R)

fn set_error_cov_pre(&mut self, val: Mat)

priori error estimate covariance matrix (P’(k)): P’(k)=A*P(k-1)*At + Q)

fn set_gain(&mut self, val: Mat)

Kalman gain matrix (K(k)): K(k)=P’(k)Htinv(H*P’(k)*Ht+R)

fn set_error_cov_post(&mut self, val: Mat)

posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P’(k)

fn set_temp1(&mut self, val: Mat)

fn set_temp2(&mut self, val: Mat)

fn set_temp3(&mut self, val: Mat)

fn set_temp4(&mut self, val: Mat)

fn set_temp5(&mut self, val: Mat)

fn init(
    &mut self,
    dynam_params: i32,
    measure_params: i32,
    control_params: i32,
    typ: i32
) -> Result<()>

Re-initializes Kalman filter. The previous content is destroyed.

Parameters

• dynamParams: Dimensionality of the state.
• measureParams: Dimensionality of the measurement.
• controlParams: Dimensionality of the control vector.
• type: Type of the created matrices that should be CV_32F or CV_64F.

C++ default parameters

• control_params: 0
• typ: CV_32F

fn predict(&mut self, control: &Mat) -> Result<Mat>

Computes a predicted state.

Parameters

• control: The optional input control

C++ default parameters

• control: Mat()

fn correct(&mut self, measurement: &Mat) -> Result<Mat>

Updates the predicted state from the measurement.

Parameters

• measurement: The measured system parameters

Implementors

impl KalmanFilterTrait for KalmanFilter