View Source Evision.KalmanFilter (Evision v0.1.38)
Summary
Functions
Updates the predicted state from the measurement.
KalmanFilter
KalmanFilter
KalmanFilter
Computes a predicted state.
Computes a predicted state.
Types
@type t() :: %Evision.KalmanFilter{ref: reference()}
Type that represents an KalmanFilter struct.
ref.
reference()The underlying erlang resource variable.
Functions
@spec correct(t(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
Updates the predicted state from the measurement.
Positional Arguments
self:
Evision.KalmanFilter.t()measurement:
Evision.Mat.t().The measured system parameters
Return
- retval:
Evision.Mat.t()
Python prototype (for reference only):
correct(measurement) -> retval@spec get_controlMatrix(t()) :: Evision.Mat.t()
@spec get_errorCovPost(t()) :: Evision.Mat.t()
@spec get_errorCovPre(t()) :: Evision.Mat.t()
@spec get_gain(t()) :: Evision.Mat.t()
@spec get_measurementMatrix(t()) :: Evision.Mat.t()
@spec get_measurementNoiseCov(t()) :: Evision.Mat.t()
@spec get_processNoiseCov(t()) :: Evision.Mat.t()
@spec get_statePost(t()) :: Evision.Mat.t()
@spec get_statePre(t()) :: Evision.Mat.t()
@spec get_transitionMatrix(t()) :: Evision.Mat.t()
KalmanFilter
Return
- self:
Evision.KalmanFilter.t()
Python prototype (for reference only):
KalmanFilter() -> <KalmanFilter object>KalmanFilter
Positional Arguments
dynamParams:
int.Dimensionality of the state.
measureParams:
int.Dimensionality of the measurement.
Keyword Arguments
controlParams:
int.Dimensionality of the control vector.
type:
int.Type of the created matrices that should be CV_32F or CV_64F.
Return
- self:
Evision.KalmanFilter.t()
Has overloading in C++
Python prototype (for reference only):
KalmanFilter(dynamParams, measureParams[, controlParams[, type]]) -> <KalmanFilter object>@spec kalmanFilter(integer(), integer(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}
KalmanFilter
Positional Arguments
dynamParams:
int.Dimensionality of the state.
measureParams:
int.Dimensionality of the measurement.
Keyword Arguments
controlParams:
int.Dimensionality of the control vector.
type:
int.Type of the created matrices that should be CV_32F or CV_64F.
Return
- self:
Evision.KalmanFilter.t()
Has overloading in C++
Python prototype (for reference only):
KalmanFilter(dynamParams, measureParams[, controlParams[, type]]) -> <KalmanFilter object>@spec predict(t()) :: Evision.Mat.t() | {:error, String.t()}
Computes a predicted state.
Positional Arguments
- self:
Evision.KalmanFilter.t()
Keyword Arguments
control:
Evision.Mat.t().The optional input control
Return
- retval:
Evision.Mat.t()
Python prototype (for reference only):
predict([, control]) -> retval@spec predict(t(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
Computes a predicted state.
Positional Arguments
- self:
Evision.KalmanFilter.t()
Keyword Arguments
control:
Evision.Mat.t().The optional input control
Return
- retval:
Evision.Mat.t()
Python prototype (for reference only):
predict([, control]) -> retval@spec set_controlMatrix(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_errorCovPost(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_errorCovPre(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_gain(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_measurementMatrix(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_measurementNoiseCov(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_processNoiseCov(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_statePost(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_statePre(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_transitionMatrix(t(), Evision.Mat.maybe_mat_in()) :: t()