73 lines
1.9 KiB
C++
73 lines
1.9 KiB
C++
/**
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* @file PriorFactor.h
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* @authors Frank Dellaert
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**/
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#pragma once
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#include <ostream>
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#include <gtsam/linear/NoiseModel.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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#include <gtsam/geometry/Pose2.h>
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namespace gtsam {
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/**
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* A class for a soft prior on any Lie type
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* It takes three template parameters:
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* T is the Lie group type for which the prior is define
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* Key (typically TypedSymbol) is used to look up T's in a Values
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* Values where the T's are stored, typically LieValues<Key,T> or a TupleValues<...>
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* The Key type is not arbitrary: we need to cast to a Symbol at linearize, so
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* a simple type like int will not work
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*/
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template<class Values, class Key>
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class PriorFactor: public NonlinearFactor1<Values, Key> {
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public:
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typedef typename Key::Value_t T;
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private:
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typedef NonlinearFactor1<Values, Key> Base;
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T prior_; /** The measurement */
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public:
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// shorthand for a smart pointer to a factor
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typedef typename boost::shared_ptr<PriorFactor> shared_ptr;
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/** Constructor */
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PriorFactor(const Key& key, const T& prior,
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const SharedGaussian& model) :
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Base(model, key), prior_(prior) {
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}
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/** implement functions needed for Testable */
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/** print */
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void print(const std::string& s) const {
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Base::print(s);
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prior_.print("prior");
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}
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/** equals */
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bool equals(const NonlinearFactor<Values>& expected, double tol) const {
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const PriorFactor<Values, Key> *e = dynamic_cast<const PriorFactor<
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Values, Key>*> (&expected);
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return e != NULL && Base::equals(expected, tol) && this->prior_.equals(
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e->prior_, tol);
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}
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/** implement functions needed to derive from Factor */
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/** vector of errors */
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Vector evaluateError(const T& p, boost::optional<Matrix&> H = boost::none) const {
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if (H) (*H) = eye(p.dim());
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// manifold equivalent of h(x)-z -> log(z,h(x))
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return prior_.logmap(p);
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}
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};
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} /// namespace gtsam
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