KalmanFilter.h
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// *****************************************************************
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// Filename: KalmanFilter.h
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// Copyright: Kai Welke, Chair Prof. Dillmann (IAIM),
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// Institute for Computer Science and Engineering (CSE),
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// University of Karlsruhe. All rights reserved.
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// Author: Kai Welke
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// Date: 27.05.2009
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// *****************************************************************
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#pragma once
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// *****************************************************************
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// includes
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// *****************************************************************
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#include <math.h>
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#include <Eigen/Eigen>
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#include "
Gaussian.h
"
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// Kalman filter for linear system with the following control model:
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// xt = Axt-1 + But + d + gaussian error
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// and following measurment model
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// zt = Cx + e + gaussian error
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// *****************************************************************
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// declaration of KalmanFilter
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// *****************************************************************
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class
KalmanFilter
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{
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public
:
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EIGEN_MAKE_ALIGNED_OPERATOR_NEW
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// construction / destruction
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KalmanFilter
() {};
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// setup system
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/**
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* @param motion_A nxn square matrix being the linear transformation of the state
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* @param motion_B nxm matrix being the transformation from control to state
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* @param motion_noise n dimensional gaussian being the motion noise
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**/
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void
setMotionModel
(
const
Eigen::MatrixXd& motion_A,
const
Eigen::MatrixXd& motion_B,
const
Gaussian
& motion_noise);
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/**
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* @param measurement_C transformation from state space to observation space
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* @param measurement_noise gaussian encoding the observation noise
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**/
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void
setMeasurementModel
(
const
Eigen::MatrixXd& measurement_C,
const
Gaussian
& measurement_noise);
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// filtering (if result == NULL, believe is updated)
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Gaussian
filter
(
const
Gaussian
& believe,
const
Eigen::VectorXd& actionU,
const
Eigen::VectorXd& perceptZ);
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Gaussian
predict
(
const
Gaussian
& believe,
const
Eigen::VectorXd& actionU);
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Gaussian
update
(
const
Gaussian
& predicted_believe,
const
Eigen::VectorXd& perceptZ);
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public
:
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// system model
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Eigen::MatrixXd
m_A
;
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Eigen::MatrixXd
m_B
;
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Eigen::MatrixXd
m_C
;
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Eigen::MatrixXd
m_A_t
;
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Eigen::MatrixXd
m_C_t
;
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// noises
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Gaussian
m_Q
;
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Gaussian
m_R
;
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};
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KalmanFilter::KalmanFilter
EIGEN_MAKE_ALIGNED_OPERATOR_NEW KalmanFilter()
Definition:
KalmanFilter.h:33
KalmanFilter::setMotionModel
void setMotionModel(const Eigen::MatrixXd &motion_A, const Eigen::MatrixXd &motion_B, const Gaussian &motion_noise)
Definition:
KalmanFilter.cpp:19
KalmanFilter
Definition:
KalmanFilter.h:27
KalmanFilter::setMeasurementModel
void setMeasurementModel(const Eigen::MatrixXd &measurement_C, const Gaussian &measurement_noise)
Definition:
KalmanFilter.cpp:29
KalmanFilter::m_C
Eigen::MatrixXd m_C
Definition:
KalmanFilter.h:58
KalmanFilter::filter
Gaussian filter(const Gaussian &believe, const Eigen::VectorXd &actionU, const Eigen::VectorXd &perceptZ)
Definition:
KalmanFilter.cpp:41
KalmanFilter::m_B
Eigen::MatrixXd m_B
Definition:
KalmanFilter.h:57
KalmanFilter::m_C_t
Eigen::MatrixXd m_C_t
Definition:
KalmanFilter.h:60
Gaussian.h
KalmanFilter::predict
Gaussian predict(const Gaussian &believe, const Eigen::VectorXd &actionU)
Definition:
KalmanFilter.cpp:52
KalmanFilter::m_A
Eigen::MatrixXd m_A
Definition:
KalmanFilter.h:56
Gaussian
Definition:
Gaussian.h:46
KalmanFilter::m_Q
Gaussian m_Q
Definition:
KalmanFilter.h:63
KalmanFilter::m_R
Gaussian m_R
Definition:
KalmanFilter.h:64
KalmanFilter::update
Gaussian update(const Gaussian &predicted_believe, const Eigen::VectorXd &perceptZ)
Definition:
KalmanFilter.cpp:63
KalmanFilter::m_A_t
Eigen::MatrixXd m_A_t
Definition:
KalmanFilter.h:59
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KalmanFilter.h
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