86 lines
2.4 KiB
C++
86 lines
2.4 KiB
C++
/**
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* @file BetweenFactor.h
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* @authors Frank Dellaert, Viorela Ila
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**/
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#pragma once
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#include <ostream>
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#include "NonlinearFactor.h"
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#include "GaussianFactor.h"
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#include "Lie.h"
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#include "Matrix.h"
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namespace gtsam {
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/**
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* A class for a measurement predicted by "between(config[key1],config[key2])"
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* T is the Lie group type, Config where the T's are gotten from
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*/
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template<class Config, class Key, class T>
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class BetweenFactor: public NonlinearFactor2<Config, Key, T, Key, T> {
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private:
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typedef NonlinearFactor2<Config, Key, T, Key, T> Base;
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T measured_; /** The measurement */
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Matrix square_root_inverse_covariance_; /** sqrt(inv(measurement_covariance)) */
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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<BetweenFactor> shared_ptr;
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/** Constructor */
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BetweenFactor(const Key& key1, const Key& key2, const T& measured,
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const Matrix& measurement_covariance) :
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Base(1, key1, key2), measured_(measured) {
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square_root_inverse_covariance_ = inverse_square_root(
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measurement_covariance);
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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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measured_.print("measured ");
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gtsam::print(square_root_inverse_covariance_, "MeasurementCovariance");
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}
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/** equals */
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bool equals(const NonlinearFactor<Config>& expected, double tol) const {
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const BetweenFactor<Config, Key, T> *e =
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dynamic_cast<const BetweenFactor<Config, Key, T>*> (&expected);
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return e != NULL && Base::equals(expected)
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&& this->measured_.equals(e->measured_, 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& p1, const T& p2, boost::optional<Matrix&> H1 =
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boost::none, boost::optional<Matrix&> H2 = boost::none) const {
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// h - z
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T hx = between(p1, p2);
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// TODO should be done by noise model
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if (H1 || H2) {
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between(p1,p2,H1,H2);
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if (H1) *H1 = square_root_inverse_covariance_ * *H1;
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if (H2) *H2 = square_root_inverse_covariance_ * *H2;
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}
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// manifold equivalent of h(x)-z -> log(z,h(x))
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// TODO use noise model, error vector is not whitened yet
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return square_root_inverse_covariance_ * logmap(measured_, hx);
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}
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/** return the measured */
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inline const T measured() const {return measured_;}
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/** number of variables attached to this factor */
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inline std::size_t size() const { return 2;}
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};
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} /// namespace gtsam
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