gtsam::ExtendedKalmanFilter< VALUE > Class Template Reference

#include <ExtendedKalmanFilter.h>

Public Types
typedef std::shared_ptr< ExtendedKalmanFilter< VALUE > >	shared_ptr
typedef VALUE	T

Static Protected Member Functions
static T	solve_ (const GaussianFactorGraph &linearFactorGraph, const Values &linearizationPoints, Key x, JacobianFactor::shared_ptr *newPrior)

Protected Attributes
T	x_
JacobianFactor::shared_ptr	priorFactor_

Standard Constructors
	ExtendedKalmanFilter (Key key_initial, T x_initial, noiseModel::Gaussian::shared_ptr P_initial)

Testable
void	print (const std::string &s="") const
	print

Interface
T	predict (const NoiseModelFactor &motionFactor)
T	update (const NoiseModelFactor &measurementFactor)
const JacobianFactor::shared_ptr	Density () const
	Return current predictive (if called after predict)/posterior (if called after update)

Detailed Description

template<class VALUE>
class gtsam::ExtendedKalmanFilter< VALUE >

This is a generic Extended Kalman Filter class implemented using nonlinear factors. GTSAM basically does SRIF with Cholesky to solve the filter problem, making this an efficient, numerically stable Kalman Filter implementation.

The Kalman Filter relies on two models: a motion model that predicts the next state using the current state, and a measurement model that predicts the measurement value at a given state. Because these two models are situation-dependent, base classes for each have been provided above, from which the user must derive a class and incorporate the actual modeling equations.

The class provides a "predict" and "update" function to perform these steps independently. TODO: a "predictAndUpdate" that combines both steps for some computational savings.

Member Function Documentation

predict()

template<class VALUE >

ExtendedKalmanFilter< VALUE >::T gtsam::ExtendedKalmanFilter< VALUE >::predict

(

const NoiseModelFactor &

motionFactor

)

Calculate predictive density \( P(x_) ~ \int P(x_min) P(x_min, x_)\) The motion model should be given as a factor with key1 for \(x_min\) and key2 for \(x_\)

update()

template<class VALUE >

ExtendedKalmanFilter< VALUE >::T gtsam::ExtendedKalmanFilter< VALUE >::update

(

const NoiseModelFactor &

measurementFactor

)

Calculate posterior density P(x_) ~ L(z|x) P(x) The likelihood L(z|x) should be given as a unary factor on x

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The documentation for this class was generated from the following files:

• /home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/nonlinear/ExtendedKalmanFilter.h
• /home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/nonlinear/ExtendedKalmanFilter-inl.h