KalmanFilterAsync extension
KalmanFilter implements a standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter. However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get an extended Kalman filter functionality.
For further details, please see: https://docs.opencv.org/4.6.0/dd/d6a/classcv_1_1KalmanFilter.html
- on
Methods
-
correctAsync(
Mat measurement) → Future< Mat> -
getControlMatrix(
) → Future< Mat> -
getErrorCovPost(
) → Future< Mat> -
getErrorCovPre(
) → Future< Mat> -
getGain(
) → Future< Mat> -
getMeasurementMatrix(
) → Future< Mat> -
getMeasurementNoiseCov(
) → Future< Mat> -
getProcessNoiseCov(
) → Future< Mat> -
getStatePost(
) → Future< Mat> -
getStatePre(
) → Future< Mat> -
getTemp1(
) → Future< Mat> -
getTemp2(
) → Future< Mat> -
getTemp3(
) → Future< Mat> -
getTemp4(
) → Future< Mat> -
getTemp5(
) → Future< Mat> -
getTransitionMatrix(
) → Future< Mat> -
initAsync(
int dynamParams, int measureParams, {int controlParams = 0, int type = MatType.CV_32F}) → Future< void> -
predictAsync(
{Mat? control}) → Future< Mat> -
setControlMatrix(
Mat m) → Future< void> -
setErrorCovPost(
Mat m) → Future< void> -
setErrorCovPre(
Mat m) → Future< void> -
setGain(
Mat m) → Future< void> -
setMeasurementMatrix(
Mat m) → Future< void> -
setMeasurementNoiseCov(
Mat m) → Future< void> -
setProcessNoiseCov(
Mat m) → Future< void> -
setStatePost(
Mat m) → Future< void> -
setStatePre(
Mat m) → Future< void> -
setTransitionMatrix(
Mat m) → Future< void>
Static Methods
-
createAsync(
int dynamParams, int measureParams, {int controlParams = 0, int type = MatType.CV_32F}) → Future< KalmanFilter>