TransformBtwRobotsUnaryFactor.h

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/* ----------------------------------------------------------------------------
 
 * GTSAM Copyright 2010, Georgia Tech Research Corporation,
 * Atlanta, Georgia 30332-0415
 * All Rights Reserved
 * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
 
 * See LICENSE for the license information
 
 * -------------------------------------------------------------------------- */

#pragma once
 
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/linear/GaussianFactor.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/Lie.h>
 
#include <ostream>
 
namespace gtsam {

  template<class VALUE>
  class TransformBtwRobotsUnaryFactor: public NonlinearFactor { // TODO why not NoiseModelFactorN ?
 
  public:
 
    typedef VALUE T;
 
  private:
 
    typedef TransformBtwRobotsUnaryFactor<VALUE> This;
    typedef gtsam::NonlinearFactor Base;
 
    gtsam::Key key_;
 
    VALUE measured_; 
 
    gtsam::Values valA_; // given values for robot A map\trajectory
    gtsam::Values valB_; // given values for robot B map\trajectory
    gtsam::Key keyA_;    // key of robot A to which the measurement refers
    gtsam::Key keyB_;    // key of robot B to which the measurement refers
 
    SharedGaussian model_;

    GTSAM_CONCEPT_LIE_TYPE(T)
    GTSAM_CONCEPT_TESTABLE_TYPE(T)
 
  public:
 
    // shorthand for a smart pointer to a factor
    typedef typename std::shared_ptr<TransformBtwRobotsUnaryFactor> shared_ptr;

    TransformBtwRobotsUnaryFactor() {}

    TransformBtwRobotsUnaryFactor(Key key, const VALUE& measured, Key keyA, Key keyB,
        const gtsam::Values& valA, const gtsam::Values& valB,
        const SharedGaussian& model) :
          Base(KeyVector{key}), key_(key), measured_(measured), keyA_(keyA), keyB_(keyB),
          model_(model){
 
      setValAValB(valA, valB);
 
    }
 
    ~TransformBtwRobotsUnaryFactor() override {}
 

    gtsam::NonlinearFactor::shared_ptr clone() const override { return std::make_shared<This>(*this); }
 


    void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const override {
      std::cout << s << "TransformBtwRobotsUnaryFactor("
          << keyFormatter(key_) << ")\n";
      std::cout << "MR between factor keys: "
          << keyFormatter(keyA_) << ","
          << keyFormatter(keyB_) << "\n";
      measured_.print("  measured: ");
      model_->print("  noise model: ");
      //      Base::print(s, keyFormatter);
    }

    bool equals(const NonlinearFactor& f, double tol=1e-9) const override {
      const This *t =  dynamic_cast<const This*> (&f);
 
      if(t && Base::equals(f))
        return key_ == t->key_ && measured_.equals(t->measured_);
      else
        return false;
    }

 
    /* ************************************************************************* */
    void setValAValB(const gtsam::Values& valA, const gtsam::Values& valB){
      if ( (!valA.exists(keyA_)) && (!valB.exists(keyA_)) && (!valA.exists(keyB_)) && (!valB.exists(keyB_)) )
        throw("something is wrong!");
 
      // TODO: make sure the two keys belong to different robots
 
      if (valA.exists(keyA_)){
        valA_ = valA;
        valB_ = valB;
      }
      else {
        valA_ = valB;
        valB_ = valA;
      }
    }
 
    /* ************************************************************************* */
    double error(const gtsam::Values& x) const override {
      return whitenedError(x).squaredNorm();
    }
 
    /* ************************************************************************* */
    /* This version of linearize recalculates the noise model each time */
    std::shared_ptr<gtsam::GaussianFactor> linearize(const gtsam::Values& x) const override {
      // Only linearize if the factor is active
      if (!this->active(x))
        return std::shared_ptr<gtsam::JacobianFactor>();
 
      //std::cout<<"About to linearize"<<std::endl;
      gtsam::Matrix A1;
      std::vector<gtsam::Matrix> A(this->size());
      gtsam::Vector b = -whitenedError(x, A);
      A1 = A[0];
 
      return gtsam::GaussianFactor::shared_ptr(
          new gtsam::JacobianFactor(key_, A1, b, gtsam::noiseModel::Unit::Create(b.size())));
    }
 
 
    /* ************************************************************************* */
    gtsam::Vector whitenedError(const gtsam::Values& x, OptionalMatrixVecType H = nullptr) const {
 
      T orgA_T_currA = valA_.at<T>(keyA_);
      T orgB_T_currB = valB_.at<T>(keyB_);
      T orgA_T_orgB = x.at<T>(key_);
 
      T currA_T_currB_pred;
      if (H) {
        Matrix H_compose, H_between1;
        T orgA_T_currB = orgA_T_orgB.compose(orgB_T_currB, H_compose, {});
        currA_T_currB_pred = orgA_T_currA.between(orgA_T_currB, {}, H_between1);
        (*H)[0] = H_compose * H_between1;
      }
      else {
        T orgA_T_currB = orgA_T_orgB.compose(orgB_T_currB);
        currA_T_currB_pred = orgA_T_currA.between(orgA_T_currB);
      }
 
      const T& currA_T_currB_msr  = measured_;
      Vector error = currA_T_currB_msr.localCoordinates(currA_T_currB_pred);
 
      if (H)
        model_->WhitenSystem(*H, error);
      else
        model_->whitenInPlace(error);
 
      return error;
    }
 
    /* ************************************************************************* */
    gtsam::Vector whitenedError(const gtsam::Values& x, std::vector<Matrix>& H) const {
          return whitenedError(x, &H);
    }
 
 
    /* ************************************************************************* */
    gtsam::Vector unwhitenedError(const gtsam::Values& x) const {
 
      T orgA_T_currA = valA_.at<T>(keyA_);
      T orgB_T_currB = valB_.at<T>(keyB_);
      T orgA_T_orgB = x.at<T>(key_);
 
      T orgA_T_currB = orgA_T_orgB.compose(orgB_T_currB);
      T currA_T_currB_pred = orgA_T_currA.between(orgA_T_currB);
 
      T currA_T_currB_msr  = measured_;
      return currA_T_currB_msr.localCoordinates(currA_T_currB_pred);
    }
 
    /* ************************************************************************* */
 
    size_t dim() const override {
      return model_->R().rows() + model_->R().cols();
    }
 
  private:
 
#if GTSAM_ENABLE_BOOST_SERIALIZATION
    friend class boost::serialization::access;
    template<class ARCHIVE>
    void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
      ar & boost::serialization::make_nvp("NonlinearFactor",
          boost::serialization::base_object<Base>(*this));
      //ar & BOOST_SERIALIZATION_NVP(measured_);
    }
#endif
  }; // \class TransformBtwRobotsUnaryFactor

  template<class VALUE>
  struct traits<TransformBtwRobotsUnaryFactor<VALUE> > :
      public Testable<TransformBtwRobotsUnaryFactor<VALUE> > {
  };
 
}