Trait opencv::prelude::KalmanFilterTrait[−][src]

pub trait KalmanFilterTrait: KalmanFilterTraitConst {

Show 19 methods    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

Provided methods

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

[src]

fn set_state_post(&mut self, val: Mat)

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

state transition matrix (A)

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

measurement matrix (H)

process noise covariance matrix (Q)

measurement noise covariance matrix (R)

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

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

[src]

fn set_error_cov_post(&mut self, val: Mat)

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

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

Computes a predicted state.

Parameters
  • control: The optional input control
C++ default parameters
  • control: Mat()

Updates the predicted state from the measurement.

Parameters
  • measurement: The measured system parameters

Implementors