Mapper.h

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/*
 * Copyright 2010 SRI International
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

#ifndef OPEN_KARTO_MAPPER_H
#define OPEN_KARTO_MAPPER_H

#include <map>
#include <vector>

#include <open_karto/Karto.h>

namespace karto
{
  // Listener classes

  class MapperListener
  {
  public:
    virtual void Info(const std::string& /*rInfo*/) {};
  };

  class MapperDebugListener
  {
  public:
    virtual void Debug(const std::string& /*rInfo*/) {};
  };

  class MapperLoopClosureListener : public MapperListener
  {
  public:
    virtual void LoopClosureCheck(const std::string& /*rInfo*/) {};

    virtual void BeginLoopClosure(const std::string& /*rInfo*/) {};

    virtual void EndLoopClosure(const std::string& /*rInfo*/) {};
  };  // MapperListener


  class EdgeLabel
  {
  public:
    EdgeLabel()
    {
    }

    virtual ~EdgeLabel()
    {
    }
  };  // EdgeLabel


  // A LinkInfo object contains the requisite information for the "spring"
  // that links two scans together--the pose difference and the uncertainty
  // (represented by a covariance matrix).
  class LinkInfo : public EdgeLabel
  {
  public:
    LinkInfo(const Pose2& rPose1, const Pose2& rPose2, const Matrix3& rCovariance)
    {
      Update(rPose1, rPose2, rCovariance);
    }

    virtual ~LinkInfo()
    {
    }

  public:
    void Update(const Pose2& rPose1, const Pose2& rPose2, const Matrix3& rCovariance)
    {
      m_Pose1 = rPose1;
      m_Pose2 = rPose2;

      // transform second pose into the coordinate system of the first pose
      Transform transform(rPose1, Pose2());
      m_PoseDifference = transform.TransformPose(rPose2);

      // transform covariance into reference of first pose
      Matrix3 rotationMatrix;
      rotationMatrix.FromAxisAngle(0, 0, 1, -rPose1.GetHeading());

      m_Covariance = rotationMatrix * rCovariance * rotationMatrix.Transpose();
    }

    inline const Pose2& GetPose1()
    {
      return m_Pose1;
    }

    inline const Pose2& GetPose2()
    {
      return m_Pose2;
    }

    inline const Pose2& GetPoseDifference()
    {
      return m_PoseDifference;
    }

    inline const Matrix3& GetCovariance()
    {
      return m_Covariance;
    }

  private:
    Pose2 m_Pose1;
    Pose2 m_Pose2;
    Pose2 m_PoseDifference;
    Matrix3 m_Covariance;
  };  // LinkInfo


  template<typename T>
  class Edge;

  template<typename T>
  class Vertex
  {
    friend class Edge<T>;

  public:
    Vertex(T* pObject)
      : m_pObject(pObject)
    {
    }

    virtual ~Vertex()
    {
    }

    inline const std::vector<Edge<T>*>& GetEdges() const
    {
      return m_Edges;
    }

    inline T* GetObject() const
    {
      return m_pObject;
    }

    std::vector<Vertex<T>*> GetAdjacentVertices() const
    {
      std::vector<Vertex<T>*> vertices;

      const_forEach(typename std::vector<Edge<T>*>, &m_Edges)
      {
        Edge<T>* pEdge = *iter;

        // check both source and target because we have a undirected graph
        if (pEdge->GetSource() != this)
        {
          vertices.push_back(pEdge->GetSource());
        }

        if (pEdge->GetTarget() != this)
        {
          vertices.push_back(pEdge->GetTarget());
        }
      }

      return vertices;
    }

  private:
    inline void AddEdge(Edge<T>* pEdge)
    {
      m_Edges.push_back(pEdge);
    }

    T* m_pObject;
    std::vector<Edge<T>*> m_Edges;
  };  // Vertex<T>


  template<typename T>
  class Edge
  {
  public:
    Edge(Vertex<T>* pSource, Vertex<T>* pTarget)
      : m_pSource(pSource)
      , m_pTarget(pTarget)
      , m_pLabel(NULL)
    {
      m_pSource->AddEdge(this);
      m_pTarget->AddEdge(this);
    }

    virtual ~Edge()
    {
      m_pSource = NULL;
      m_pTarget = NULL;

      if (m_pLabel != NULL)
      {
        delete m_pLabel;
        m_pLabel = NULL;
      }
    }

  public:
    inline Vertex<T>* GetSource() const
    {
      return m_pSource;
    }

    inline Vertex<T>* GetTarget() const
    {
      return m_pTarget;
    }

    inline EdgeLabel* GetLabel()
    {
      return m_pLabel;
    }

    inline void SetLabel(EdgeLabel* pLabel)
    {
      m_pLabel = pLabel;
    }

  private:
    Vertex<T>* m_pSource;
    Vertex<T>* m_pTarget;
    EdgeLabel* m_pLabel;
  };  // class Edge<T>


  template<typename T>
  class Visitor
  {
  public:
    virtual kt_bool Visit(Vertex<T>* pVertex) = 0;
  };  // Visitor<T>


  template<typename T>
  class Graph;

  template<typename T>
  class GraphTraversal
  {
  public:
    GraphTraversal(Graph<T>* pGraph)
      : m_pGraph(pGraph)
    {
    }

    virtual ~GraphTraversal()
    {
    }

  public:
    virtual std::vector<T*> Traverse(Vertex<T>* pStartVertex, Visitor<T>* pVisitor) = 0;

  protected:
    Graph<T>* m_pGraph;
  };  // GraphTraversal<T>


  template<typename T>
  class Graph
  {
  public:
    typedef std::map<Name, std::vector<Vertex<T>*> > VertexMap;

  public:
    Graph()
    {
    }

    virtual ~Graph()
    {
      Clear();
    }

  public:
    inline void AddVertex(const Name& rName, Vertex<T>* pVertex)
    {
      m_Vertices[rName].push_back(pVertex);
    }

    inline void AddEdge(Edge<T>* pEdge)
    {
      m_Edges.push_back(pEdge);
    }

    void Clear()
    {
      forEachAs(typename VertexMap, &m_Vertices, indexIter)
      {
        // delete each vertex
        forEach(typename std::vector<Vertex<T>*>, &(indexIter->second))
        {
          delete *iter;
        }
      }
      m_Vertices.clear();

      const_forEach(typename std::vector<Edge<T>*>, &m_Edges)
      {
        delete *iter;
      }
      m_Edges.clear();
    }

    inline const std::vector<Edge<T>*>& GetEdges() const
    {
      return m_Edges;
    }

    inline const VertexMap& GetVertices() const
    {
      return m_Vertices;
    }

  protected:
    VertexMap m_Vertices;

    std::vector<Edge<T>*> m_Edges;
  };  // Graph<T>


  class Mapper;
  class ScanMatcher;

  class KARTO_EXPORT MapperGraph : public Graph<LocalizedRangeScan>
  {
  public:
    MapperGraph(Mapper* pMapper, kt_double rangeThreshold);

    virtual ~MapperGraph();

  public:
    void AddVertex(LocalizedRangeScan* pScan);

    Edge<LocalizedRangeScan>* AddEdge(LocalizedRangeScan* pSourceScan,
                                      LocalizedRangeScan* pTargetScan,
                                      kt_bool& rIsNewEdge);

    void AddEdges(LocalizedRangeScan* pScan, const Matrix3& rCovariance);

    kt_bool TryCloseLoop(LocalizedRangeScan* pScan, const Name& rSensorName);

    LocalizedRangeScanVector FindNearLinkedScans(LocalizedRangeScan* pScan, kt_double maxDistance);

    inline ScanMatcher* GetLoopScanMatcher() const
    {
      return m_pLoopScanMatcher;
    }

  private:
    inline Vertex<LocalizedRangeScan>* GetVertex(LocalizedRangeScan* pScan)
    {
      return m_Vertices[pScan->GetSensorName()][pScan->GetStateId()];
    }

    LocalizedRangeScan* GetClosestScanToPose(const LocalizedRangeScanVector& rScans, const Pose2& rPose) const;

    void LinkScans(LocalizedRangeScan* pFromScan,
                   LocalizedRangeScan* pToScan,
                   const Pose2& rMean,
                   const Matrix3& rCovariance);

    void LinkNearChains(LocalizedRangeScan* pScan, Pose2Vector& rMeans, std::vector<Matrix3>& rCovariances);

    void LinkChainToScan(const LocalizedRangeScanVector& rChain,
                         LocalizedRangeScan* pScan,
                         const Pose2& rMean,
                         const Matrix3& rCovariance);

    std::vector<LocalizedRangeScanVector> FindNearChains(LocalizedRangeScan* pScan);

    Pose2 ComputeWeightedMean(const Pose2Vector& rMeans, const std::vector<Matrix3>& rCovariances) const;

    LocalizedRangeScanVector FindPossibleLoopClosure(LocalizedRangeScan* pScan,
                                                     const Name& rSensorName,
                                                     kt_int32u& rStartNum);

    void CorrectPoses();

  private:
    Mapper* m_pMapper;

    ScanMatcher* m_pLoopScanMatcher;

    GraphTraversal<LocalizedRangeScan>* m_pTraversal;
  };  // MapperGraph


  class ScanSolver
  {
  public:
    typedef std::vector<std::pair<kt_int32s, Pose2> > IdPoseVector;

    ScanSolver()
    {
    }

    virtual ~ScanSolver()
    {
    }

  public:
    virtual void Compute() = 0;

    virtual const IdPoseVector& GetCorrections() const = 0;

    virtual void AddNode(Vertex<LocalizedRangeScan>* /*pVertex*/)
    {
    }

    virtual void RemoveNode(kt_int32s /*id*/)
    {
    }

    virtual void AddConstraint(Edge<LocalizedRangeScan>* /*pEdge*/)
    {
    }

    virtual void RemoveConstraint(kt_int32s /*sourceId*/, kt_int32s /*targetId*/)
    {
    }

    virtual void Clear() {};
  };  // ScanSolver


  class CorrelationGrid : public Grid<kt_int8u>
  {
  public:
    virtual ~CorrelationGrid()
    {
      delete [] m_pKernel;
    }

  public:
    static CorrelationGrid* CreateGrid(kt_int32s width,
                                       kt_int32s height,
                                       kt_double resolution,
                                       kt_double smearDeviation)
    {
      assert(resolution != 0.0);

      // +1 in case of roundoff
      kt_int32u borderSize = GetHalfKernelSize(smearDeviation, resolution) + 1;

      CorrelationGrid* pGrid = new CorrelationGrid(width, height, borderSize, resolution, smearDeviation);

      return pGrid;
    }

    virtual kt_int32s GridIndex(const Vector2<kt_int32s>& rGrid, kt_bool boundaryCheck = true) const
    {
      kt_int32s x = rGrid.GetX() + m_Roi.GetX();
      kt_int32s y = rGrid.GetY() + m_Roi.GetY();

      return Grid<kt_int8u>::GridIndex(Vector2<kt_int32s>(x, y), boundaryCheck);
    }

    inline const Rectangle2<kt_int32s>& GetROI() const
    {
      return m_Roi;
    }

    inline void SetROI(const Rectangle2<kt_int32s>& roi)
    {
      m_Roi = roi;
    }

    inline void SmearPoint(const Vector2<kt_int32s>& rGridPoint)
    {
      assert(m_pKernel != NULL);

      int gridIndex = GridIndex(rGridPoint);
      if (GetDataPointer()[gridIndex] != GridStates_Occupied)
      {
        return;
      }

      kt_int32s halfKernel = m_KernelSize / 2;

      // apply kernel
      for (kt_int32s j = -halfKernel; j <= halfKernel; j++)
      {
        kt_int8u* pGridAdr = GetDataPointer(Vector2<kt_int32s>(rGridPoint.GetX(), rGridPoint.GetY() + j));

        kt_int32s kernelConstant = (halfKernel) + m_KernelSize * (j + halfKernel);

        // if a point is on the edge of the grid, there is no problem
        // with running over the edge of allowable memory, because
        // the grid has margins to compensate for the kernel size
        for (kt_int32s i = -halfKernel; i <= halfKernel; i++)
        {
          kt_int32s kernelArrayIndex = i + kernelConstant;

          kt_int8u kernelValue = m_pKernel[kernelArrayIndex];
          if (kernelValue > pGridAdr[i])
          {
            // kernel value is greater, so set it to kernel value
            pGridAdr[i] = kernelValue;
          }
        }
      }
    }

  protected:
    CorrelationGrid(kt_int32u width, kt_int32u height, kt_int32u borderSize,
                    kt_double resolution, kt_double smearDeviation)
      : Grid<kt_int8u>(width + borderSize * 2, height + borderSize * 2)
      , m_SmearDeviation(smearDeviation)
      , m_pKernel(NULL)
    {
      GetCoordinateConverter()->SetScale(1.0 / resolution);

      // setup region of interest
      m_Roi = Rectangle2<kt_int32s>(borderSize, borderSize, width, height);

      // calculate kernel
      CalculateKernel();
    }

    virtual void CalculateKernel()
    {
      kt_double resolution = GetResolution();

      assert(resolution != 0.0);
      assert(m_SmearDeviation != 0.0);

      // min and max distance deviation for smearing;
      // will smear for two standard deviations, so deviation must be at least 1/2 of the resolution
      const kt_double MIN_SMEAR_DISTANCE_DEVIATION = 0.5 * resolution;
      const kt_double MAX_SMEAR_DISTANCE_DEVIATION = 10 * resolution;

      // check if given value too small or too big
      if (!math::InRange(m_SmearDeviation, MIN_SMEAR_DISTANCE_DEVIATION, MAX_SMEAR_DISTANCE_DEVIATION))
      {
        std::stringstream error;
        error << "Mapper Error:  Smear deviation too small:  Must be between "
              << MIN_SMEAR_DISTANCE_DEVIATION
              << " and "
              << MAX_SMEAR_DISTANCE_DEVIATION;
        throw std::runtime_error(error.str());
      }

      // NOTE:  Currently assumes a two-dimensional kernel

      // +1 for center
      m_KernelSize = 2 * GetHalfKernelSize(m_SmearDeviation, resolution) + 1;

      // allocate kernel
      m_pKernel = new kt_int8u[m_KernelSize * m_KernelSize];
      if (m_pKernel == NULL)
      {
        throw std::runtime_error("Unable to allocate memory for kernel!");
      }

      // calculate kernel
      kt_int32s halfKernel = m_KernelSize / 2;
      for (kt_int32s i = -halfKernel; i <= halfKernel; i++)
      {
        for (kt_int32s j = -halfKernel; j <= halfKernel; j++)
        {
#ifdef WIN32
          kt_double distanceFromMean = _hypot(i * resolution, j * resolution);
#else
          kt_double distanceFromMean = hypot(i * resolution, j * resolution);
#endif
          kt_double z = exp(-0.5 * pow(distanceFromMean / m_SmearDeviation, 2));

          kt_int32u kernelValue = static_cast<kt_int32u>(math::Round(z * GridStates_Occupied));
          assert(math::IsUpTo(kernelValue, static_cast<kt_int32u>(255)));

          int kernelArrayIndex = (i + halfKernel) + m_KernelSize * (j + halfKernel);
          m_pKernel[kernelArrayIndex] = static_cast<kt_int8u>(kernelValue);
        }
      }
    }

    static kt_int32s GetHalfKernelSize(kt_double smearDeviation, kt_double resolution)
    {
      assert(resolution != 0.0);

      return static_cast<kt_int32s>(math::Round(2.0 * smearDeviation / resolution));
    }

  private:
    kt_double m_SmearDeviation;

    // Size of one side of the kernel
    kt_int32s m_KernelSize;

    // Cached kernel for smearing
    kt_int8u* m_pKernel;

    // region of interest
    Rectangle2<kt_int32s> m_Roi;
  };  // CorrelationGrid


  class KARTO_EXPORT ScanMatcher
  {
  public:
    virtual ~ScanMatcher();

  public:
    static ScanMatcher* Create(Mapper* pMapper,
                               kt_double searchSize,
                               kt_double resolution,
                               kt_double smearDeviation,
                               kt_double rangeThreshold);

    kt_double MatchScan(LocalizedRangeScan* pScan,
                        const LocalizedRangeScanVector& rBaseScans,
                        Pose2& rMean, Matrix3& rCovariance,
                        kt_bool doPenalize = true,
                        kt_bool doRefineMatch = true);

    kt_double CorrelateScan(LocalizedRangeScan* pScan,
                            const Pose2& rSearchCenter,
                            const Vector2<kt_double>& rSearchSpaceOffset,
                            const Vector2<kt_double>& rSearchSpaceResolution,
                            kt_double searchAngleOffset,
                            kt_double searchAngleResolution,
                            kt_bool doPenalize,
                            Pose2& rMean,
                            Matrix3& rCovariance,
                            kt_bool doingFineMatch);

    void ComputePositionalCovariance(const Pose2& rBestPose,
                                     kt_double bestResponse,
                                     const Pose2& rSearchCenter,
                                     const Vector2<kt_double>& rSearchSpaceOffset,
                                     const Vector2<kt_double>& rSearchSpaceResolution,
                                     kt_double searchAngleResolution,
                                     Matrix3& rCovariance);

    void ComputeAngularCovariance(const Pose2& rBestPose,
                                  kt_double bestResponse,
                                  const Pose2& rSearchCenter,
                                  kt_double searchAngleOffset,
                                  kt_double searchAngleResolution,
                                  Matrix3& rCovariance);

    inline CorrelationGrid* GetCorrelationGrid() const
    {
      return m_pCorrelationGrid;
    }

  private:
    void AddScans(const LocalizedRangeScanVector& rScans, Vector2<kt_double> viewPoint);

    void AddScan(LocalizedRangeScan* pScan, const Vector2<kt_double>& rViewPoint, kt_bool doSmear = true);

    PointVectorDouble FindValidPoints(LocalizedRangeScan* pScan, const Vector2<kt_double>& rViewPoint) const;

    kt_double GetResponse(kt_int32u angleIndex, kt_int32s gridPositionIndex) const;

  protected:
    ScanMatcher(Mapper* pMapper)
      : m_pMapper(pMapper)
      , m_pCorrelationGrid(NULL)
      , m_pSearchSpaceProbs(NULL)
      , m_pGridLookup(NULL)
    {
    }

  private:
    Mapper* m_pMapper;

    CorrelationGrid* m_pCorrelationGrid;
    Grid<kt_double>* m_pSearchSpaceProbs;

    GridIndexLookup<kt_int8u>* m_pGridLookup;
  };  // ScanMatcher


  class ScanManager;

  class KARTO_EXPORT MapperSensorManager  // : public SensorManager
  {
    typedef std::map<Name, ScanManager*> ScanManagerMap;

  public:
    MapperSensorManager(kt_int32u runningBufferMaximumSize, kt_double runningBufferMaximumDistance)
      : m_RunningBufferMaximumSize(runningBufferMaximumSize)
      , m_RunningBufferMaximumDistance(runningBufferMaximumDistance)
      , m_NextScanId(0)
    {
    }

    virtual ~MapperSensorManager()
    {
      Clear();
    }

  public:
    void RegisterSensor(const Name& rSensorName);

    LocalizedRangeScan* GetScan(const Name& rSensorName, kt_int32s scanIndex);

    inline std::vector<Name> GetSensorNames()
    {
      std::vector<Name> deviceNames;
      const_forEach(ScanManagerMap, &m_ScanManagers)
      {
        deviceNames.push_back(iter->first);
      }

      return deviceNames;
    }

    LocalizedRangeScan* GetLastScan(const Name& rSensorName);

    inline void SetLastScan(LocalizedRangeScan* pScan);

    inline LocalizedRangeScan* GetScan(kt_int32s id)
    {
      assert(math::IsUpTo(id, (kt_int32s)m_Scans.size()));

      return m_Scans[id];
    }

    void AddScan(LocalizedRangeScan* pScan);

    void AddRunningScan(LocalizedRangeScan* pScan);

    LocalizedRangeScanVector& GetScans(const Name& rSensorName);

    LocalizedRangeScanVector& GetRunningScans(const Name& rSensorName);

    LocalizedRangeScanVector GetAllScans();

    void Clear();

  private:
    inline ScanManager* GetScanManager(LocalizedRangeScan* pScan)
    {
      return GetScanManager(pScan->GetSensorName());
    }

    inline ScanManager* GetScanManager(const Name& rSensorName)
    {
      if (m_ScanManagers.find(rSensorName) != m_ScanManagers.end())
      {
        return m_ScanManagers[rSensorName];
      }

      return NULL;
    }

  private:
    // map from device ID to scan data
    ScanManagerMap m_ScanManagers;

    kt_int32u m_RunningBufferMaximumSize;
    kt_double m_RunningBufferMaximumDistance;

    kt_int32s m_NextScanId;

    std::vector<LocalizedRangeScan*> m_Scans;
  };  // MapperSensorManager


  class KARTO_EXPORT Mapper : public Module
  {
    friend class MapperGraph;
    friend class ScanMatcher;

  public:
    Mapper();

    Mapper(const std::string& rName);

    virtual ~Mapper();

  public:
    void Initialize(kt_double rangeThreshold);

    void Reset();

    virtual kt_bool Process(LocalizedRangeScan* pScan);

    virtual kt_bool Process(Object* pObject);

    virtual const LocalizedRangeScanVector GetAllProcessedScans() const;

    void AddListener(MapperListener* pListener);

    void RemoveListener(MapperListener* pListener);

    void SetScanSolver(ScanSolver* pSolver);

    virtual MapperGraph* GetGraph() const;

    virtual ScanMatcher* GetSequentialScanMatcher() const;

    virtual ScanMatcher* GetLoopScanMatcher() const;

    inline MapperSensorManager* GetMapperSensorManager() const
    {
      return m_pMapperSensorManager;
    }

    inline kt_bool TryCloseLoop(LocalizedRangeScan* pScan, const Name& rSensorName)
    {
      return m_pGraph->TryCloseLoop(pScan, rSensorName);
    }

  private:
    void InitializeParameters();

    kt_bool HasMovedEnough(LocalizedRangeScan* pScan, LocalizedRangeScan* pLastScan) const;

  public:
    // fire information for listeners!!

    void FireInfo(const std::string& rInfo) const;

    void FireDebug(const std::string& rInfo) const;

    void FireLoopClosureCheck(const std::string& rInfo) const;

    void FireBeginLoopClosure(const std::string& rInfo) const;

    void FireEndLoopClosure(const std::string& rInfo) const;

    // FireRunningScansUpdated

    // FireCovarianceCalculated

    // FireLoopClosureChain

  private:
    Mapper(const Mapper&);

    const Mapper& operator=(const Mapper&);

  public:
    void SetUseScanMatching(kt_bool val) { m_pUseScanMatching->SetValue(val); }

  private:
    kt_bool m_Initialized;

    ScanMatcher* m_pSequentialScanMatcher;

    MapperSensorManager* m_pMapperSensorManager;

    MapperGraph* m_pGraph;
    ScanSolver* m_pScanOptimizer;

    std::vector<MapperListener*> m_Listeners;


    Parameter<kt_bool>* m_pUseScanMatching;

    Parameter<kt_bool>* m_pUseScanBarycenter;

    Parameter<kt_double>* m_pMinimumTimeInterval;

    Parameter<kt_double>* m_pMinimumTravelDistance;

    Parameter<kt_double>* m_pMinimumTravelHeading;

    Parameter<kt_int32u>* m_pScanBufferSize;

    Parameter<kt_double>* m_pScanBufferMaximumScanDistance;

    Parameter<kt_double>* m_pLinkMatchMinimumResponseFine;

    Parameter<kt_double>* m_pLinkScanMaximumDistance;

    Parameter<kt_bool>* m_pDoLoopClosing;

    Parameter<kt_double>* m_pLoopSearchMaximumDistance;

    Parameter<kt_int32u>* m_pLoopMatchMinimumChainSize;

    Parameter<kt_double>* m_pLoopMatchMaximumVarianceCoarse;

    Parameter<kt_double>* m_pLoopMatchMinimumResponseCoarse;

    Parameter<kt_double>* m_pLoopMatchMinimumResponseFine;

    //    CorrelationParameters correlationParameters;

    Parameter<kt_double>* m_pCorrelationSearchSpaceDimension;

    Parameter<kt_double>* m_pCorrelationSearchSpaceResolution;

    Parameter<kt_double>* m_pCorrelationSearchSpaceSmearDeviation;


    //    CorrelationParameters loopCorrelationParameters;

    Parameter<kt_double>* m_pLoopSearchSpaceDimension;

    Parameter<kt_double>* m_pLoopSearchSpaceResolution;

    Parameter<kt_double>* m_pLoopSearchSpaceSmearDeviation;

    // ScanMatcherParameters;

    // Variance of penalty for deviating from odometry when scan-matching.
    // The penalty is a multiplier (less than 1.0) is a function of the
    // delta of the scan position being tested and the odometric pose
    Parameter<kt_double>* m_pDistanceVariancePenalty;
    Parameter<kt_double>* m_pAngleVariancePenalty;

    // The range of angles to search during a coarse search and a finer search
    Parameter<kt_double>* m_pFineSearchAngleOffset;
    Parameter<kt_double>* m_pCoarseSearchAngleOffset;

    // Resolution of angles to search during a coarse search
    Parameter<kt_double>* m_pCoarseAngleResolution;

    // Minimum value of the penalty multiplier so scores do not
    // become too small
    Parameter<kt_double>* m_pMinimumAnglePenalty;
    Parameter<kt_double>* m_pMinimumDistancePenalty;

    // whether to increase the search space if no good matches are initially found
    Parameter<kt_bool>* m_pUseResponseExpansion;

  public:
    /* Abstract methods for parameter setters and getters */

    /* Getters */
    // General Parameters
    bool getParamUseScanMatching();
    bool getParamUseScanBarycenter();
    double getParamMinimumTimeInterval();
    double getParamMinimumTravelDistance();
    double getParamMinimumTravelHeading();
    int getParamScanBufferSize();
    double getParamScanBufferMaximumScanDistance();
    double getParamLinkMatchMinimumResponseFine();
    double getParamLinkScanMaximumDistance();
    double getParamLoopSearchMaximumDistance();
    bool getParamDoLoopClosing();
    int getParamLoopMatchMinimumChainSize();
    double getParamLoopMatchMaximumVarianceCoarse();
    double getParamLoopMatchMinimumResponseCoarse();
    double getParamLoopMatchMinimumResponseFine();

    // Correlation Parameters - Correlation Parameters
    double getParamCorrelationSearchSpaceDimension();
    double getParamCorrelationSearchSpaceResolution();
    double getParamCorrelationSearchSpaceSmearDeviation();

    // Correlation Parameters - Loop Closure Parameters
    double getParamLoopSearchSpaceDimension();
    double getParamLoopSearchSpaceResolution();
    double getParamLoopSearchSpaceSmearDeviation();

    // Scan Matcher Parameters
    double getParamDistanceVariancePenalty();
    double getParamAngleVariancePenalty();
    double getParamFineSearchAngleOffset();
    double getParamCoarseSearchAngleOffset();
    double getParamCoarseAngleResolution();
    double getParamMinimumAnglePenalty();
    double getParamMinimumDistancePenalty();
    bool getParamUseResponseExpansion();

    /* Setters */
    // General Parameters
    void setParamUseScanMatching(bool b);
    void setParamUseScanBarycenter(bool b);
    void setParamMinimumTimeInterval(double d);
    void setParamMinimumTravelDistance(double d);
    void setParamMinimumTravelHeading(double d);
    void setParamScanBufferSize(int i);
    void setParamScanBufferMaximumScanDistance(double d);
    void setParamLinkMatchMinimumResponseFine(double d);
    void setParamLinkScanMaximumDistance(double d);
    void setParamLoopSearchMaximumDistance(double d);
    void setParamDoLoopClosing(bool b);
    void setParamLoopMatchMinimumChainSize(int i);
    void setParamLoopMatchMaximumVarianceCoarse(double d);
    void setParamLoopMatchMinimumResponseCoarse(double d);
    void setParamLoopMatchMinimumResponseFine(double d);

    // Correlation Parameters - Correlation Parameters
    void setParamCorrelationSearchSpaceDimension(double d);
    void setParamCorrelationSearchSpaceResolution(double d);
    void setParamCorrelationSearchSpaceSmearDeviation(double d);

    // Correlation Parameters - Loop Closure Parameters
    void setParamLoopSearchSpaceDimension(double d);
    void setParamLoopSearchSpaceResolution(double d);
    void setParamLoopSearchSpaceSmearDeviation(double d);

    // Scan Matcher Parameters
    void setParamDistanceVariancePenalty(double d);
    void setParamAngleVariancePenalty(double d);
    void setParamFineSearchAngleOffset(double d);
    void setParamCoarseSearchAngleOffset(double d);
    void setParamCoarseAngleResolution(double d);
    void setParamMinimumAnglePenalty(double d);
    void setParamMinimumDistancePenalty(double d);
    void setParamUseResponseExpansion(bool b);
  };
}  // namespace karto

#endif  // OPEN_KARTO_MAPPER_H