81 lines
2.3 KiB
C++
81 lines
2.3 KiB
C++
/**
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* @file Pose3Factor.h
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* @brief A Nonlinear Factor, specialized for Urban application
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* @author Frank Dellaert
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* @author Viorela Ila
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*/
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#pragma once
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#include <map>
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#include "NonlinearFactor.h"
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#include "GaussianFactor.h"
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#include "VectorConfig.h"
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#include "Pose3.h"
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#include "Matrix.h"
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namespace gtsam {
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typedef VectorConfig Config;
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class Pose3Factor: public NonlinearFactor<Config> {
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private:
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std::string key1_, key2_; /** The keys of the two poses, order matters */
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Pose3 measured_;
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Matrix square_root_inverse_covariance_; /** sqrt(inv(measurement_covariance)) */
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public:
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typedef boost::shared_ptr<Pose3Factor> shared_ptr; // shorthand for a smart pointer to a factor
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Pose3Factor(const std::string& key1, const std::string& key2,
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const Pose3& measured, const Matrix& measurement_covariance) :
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key1_(key1), key2_(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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void print(const std::string& name) const {
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std::cout << name << std::endl;
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std::cout << "Factor " << std::endl;
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std::cout << "key1 " << key1_ << std::endl;
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std::cout << "key2 " << key2_ << std::endl;
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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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bool equals(const NonlinearFactor<Config>& expected, double tol) const {
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return false;
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}
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/** implement functions needed to derive from Factor */
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Vector error_vector(const Config& config) const {
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//z-h
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Pose3 p1 = config.get(key1_), p2 = config.get(key2_);
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return (measured_ - between(p1, p2)).vector();
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}
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std::list<std::string> keys() const {
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std::list<std::string> l;
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return l;
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}
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std::size_t size() const {
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return 2;
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}
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/** linearize */
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boost::shared_ptr<GaussianFactor> linearize(const Config& config) const {
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Pose3 p1 = config.get(key1_), p2 = config.get(key2_);
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Vector b = (measured_ - between(p1, p2)).vector();
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Matrix H1 = Dbetween1(p1, p2);
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Matrix H2 = Dbetween2(p1, p2);
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boost::shared_ptr<GaussianFactor> linearized(new GaussianFactor(key1_,
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square_root_inverse_covariance_ * H1, key2_,
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square_root_inverse_covariance_ * H2, b, 1.0));
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return linearized;
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}
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};
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} /// namespace gtsam
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