MeasurementModel.h

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/*
 * Copyright (C) 2016-2019 Istituto Italiano di Tecnologia (IIT)
 *
 * This software may be modified and distributed under the terms of the
 * BSD 3-Clause license. See the accompanying LICENSE file for details.
 */
 
#ifndef MEASUREMENTMODEL_H
#define MEASUREMENTMODEL_H
 
#include <BayesFilters/Data.h>
#include <BayesFilters/Logger.h>
#include <BayesFilters/VectorDescription.h>
 
#include <memory>
#include <string>
#include <utility>
 
#include <Eigen/Dense>
 
namespace bfl {
    class MeasurementModel;
}
 
 
class bfl::MeasurementModel : public Logger
{
public:
    virtual ~MeasurementModel() noexcept = default;
 
    virtual bool freeze(const Data& data = Data()) = 0;
 
    virtual std::pair<bool, Data> measure(const Data& data = Data()) const = 0;
 
    virtual std::pair<bool, Data> predictedMeasure(const Eigen::Ref<const Eigen::MatrixXd>& cur_states) const = 0;
 
    virtual std::pair<bool, Data> innovation(const Data& predicted_measurements, const Data& measurements) const = 0;
 
    virtual std::pair<bool, Eigen::MatrixXd> getNoiseCovarianceMatrix() const;
 
    virtual bool setProperty(const std::string& property);
 
    /* Returns the vector description of the input to the measurement equation. */
    virtual VectorDescription getInputDescription() const;
 
    /* Returns the vector description of the output measurement of the measurement equation. */
    virtual VectorDescription getMeasurementDescription() const;
};
 
#endif /* MEASUREMENTMODEL_H */