filter_base.h

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/*
 * Copyright (c) 2015, Charles River Analytics, Inc.
 * All rights reserved.
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 * modification, are permitted provided that the following conditions
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 *
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 * notice, this list of conditions and the following disclaimer.
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 * copyright notice, this list of conditions and the following
 * disclaimer in the documentation and/or other materials provided
 * with the distribution.
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 * from this software without specific prior written permission.
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 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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#ifndef RobotLocalization_FilterBase_h
#define RobotLocalization_FilterBase_h

#include "robot_localization/filter_utilities.h"

#include <Eigen/Dense>

#include <ostream>
#include <vector>
#include <set>
#include <map>
#include <queue>
#include <limits>
#include <string>

namespace RobotLocalization
{
  struct Measurement
  {
    // The topic name for this measurement. Needed
    // for capturing previous state values for new
    // measurements.
    std::string topicName_;

    // The measurement and its associated covariance
    Eigen::VectorXd measurement_;
    Eigen::MatrixXd covariance_;

    // This defines which variables within this measurement
    // actually get passed into the filter. std::vector<bool>
    // is generally frowned upon, so we use ints.
    std::vector<int> updateVector_;

    // The real-valued time, in seconds, since some epoch
    // (presumably the start of execution, but any will do)
    double time_;

    // The Mahalanobis distance threshold in number of sigmas
    double mahalanobisThresh_;

    // We want earlier times to have greater priority
    bool operator()(const Measurement &a, const Measurement &b)
    {
      return a.time_ > b.time_;
    }

    Measurement() :
      topicName_(""),
      time_(0),
      mahalanobisThresh_(std::numeric_limits<double>::max())
    {
    }
  };

  class FilterBase
  {
    public:
      FilterBase();

      virtual ~FilterBase();

      virtual void correct(const Measurement &measurement) = 0;

      bool getDebug();

      const Eigen::MatrixXd& getEstimateErrorCovariance();

      bool getInitializedStatus();

      double getLastMeasurementTime();

      double getLastUpdateTime();

      const Eigen::VectorXd& getPredictedState();

      const Eigen::MatrixXd& getProcessNoiseCovariance();

      double getSensorTimeout();

      const Eigen::VectorXd& getState();

      virtual void predict(const double delta) = 0;

      virtual void processMeasurement(const Measurement &measurement);

      void setDebug(const bool debug, std::ostream *outStream = NULL);

      void setEstimateErrorCovariance(const Eigen::MatrixXd &estimateErrorCovariance);

      void setLastMeasurementTime(const double lastMeasurementTime);

      void setLastUpdateTime(const double lastUpdateTime);

      void setProcessNoiseCovariance(const Eigen::MatrixXd &processNoiseCovariance);

      void setSensorTimeout(const double sensorTimeout);

      void setState(const Eigen::VectorXd &state);

      void validateDelta(double &delta);

    protected:

      virtual void wrapStateAngles();

      virtual bool checkMahalanobisThreshold(const Eigen::VectorXd &innovation,
                                             const Eigen::MatrixXd &invCovariance,
                                             const double nsigmas);

      Eigen::MatrixXd covarianceEpsilon_;

      std::ostream *debugStream_;

      Eigen::MatrixXd estimateErrorCovariance_;

      Eigen::MatrixXd identity_;

      bool initialized_;

      double lastMeasurementTime_;

      double lastUpdateTime_;

      Eigen::VectorXd predictedState_;

      Eigen::MatrixXd processNoiseCovariance_;

      double sensorTimeout_;

      Eigen::VectorXd state_;

      Eigen::MatrixXd transferFunction_;

      Eigen::MatrixXd transferFunctionJacobian_;

    private:

      bool debug_;
  };
}

#endif