gtsam 4.1.1
gtsam

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 situationdependent, 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.
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) 
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.  
T  update (const NoiseModelFactor &measurementFactor) 
Calculate posterior density P(x_) ~ L(zx) P(x) The likelihood L(zx) should be given as a unary factor on x.  
const JacobianFactor::shared_ptr  Density () const 
Return current predictive (if called after predict)/posterior (if called after update)  
Public Types  
typedef boost::shared_ptr< ExtendedKalmanFilter< VALUE > >  shared_ptr 
typedef VALUE  T 
typedef NoiseModelFactor2< VALUE, VALUE >  MotionFactor 
typedef NoiseModelFactor1< VALUE >  MeasurementFactor 
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_ 