44 template <
class VALUE>
48 BOOST_CONCEPT_ASSERT((IsManifold<VALUE>));
51 typedef boost::shared_ptr<ExtendedKalmanFilter<VALUE> > shared_ptr;
76 void print(
const std::string& s =
"")
const {
77 std::cout << s <<
"\n";
79 priorFactor_->print(s +
"density");
A non-templated config holding any types of Manifold-group elements.
Definition: Values.h:70
boost::shared_ptr< This > shared_ptr
shared_ptr to this class
Definition: JacobianFactor.h:93
std::uint64_t Key
Integer nonlinear key type.
Definition: types.h:57
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...
Definition: ExtendedKalmanFilter-inl.h:80
A Linear Factor Graph is a factor graph where all factors are Gaussian, i.e.
Definition: GaussianFactorGraph.h:65
A convenient base class for creating your own NoiseModelFactor with 1 variable.
Definition: NonlinearFactor.h:276
Factor Graph Constsiting of non-linear factors.
T 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 fact...
Definition: ExtendedKalmanFilter-inl.h:105
A nonlinear sum-of-squares factor with a zero-mean noise model implementing the density Templated on...
Definition: NonlinearFactor.h:161
Class to perform generic Kalman Filtering using nonlinear factor graphs.
Definition: Testable.h:57
Non-linear factor base classes.
const JacobianFactor::shared_ptr Density() const
Return current predictive (if called after predict)/posterior (if called after update)
Definition: ExtendedKalmanFilter.h:99
Global functions in a separate testing namespace.
Definition: chartTesting.h:28
void print(const std::string &s="") const
print
Definition: ExtendedKalmanFilter.h:76
This is a generic Extended Kalman Filter class implemented using nonlinear factors.
Definition: ExtendedKalmanFilter.h:45