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 * $Id: moving_least_squares.h 36097 2011-02-20 14:18:58Z marton $
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#ifndef PCL_ROS_MOVING_LEAST_SQUARES_H_
#define PCL_ROS_MOVING_LEAST_SQUARES_H_

#include "pcl_ros/pcl_nodelet.h"

// PCL includes
#include <pcl/surface/mls.h>

// Dynamic reconfigure
#include <dynamic_reconfigure/server.h>
#include "pcl_ros/MLSConfig.h"   

namespace pcl_ros
{
  namespace sync_policies = message_filters::sync_policies;

  /** \brief @b MovingLeastSquares represents a nodelet using the MovingLeastSquares implementation.
    * The type of the output is the same as the input, it only smooths the XYZ coordinates according to the parameters.
    * Normals are estimated at each point as well and published on a separate topic.
    * \author Radu Bogdan Rusu, Zoltan-Csaba Marton
    */
  class MovingLeastSquares : public PCLNodelet
  {
    typedef pcl::PointXYZ PointIn;
    typedef pcl::PointNormal NormalOut;

    typedef pcl::PointCloud<PointIn> PointCloudIn;
    typedef PointCloudIn::Ptr PointCloudInPtr;
    typedef PointCloudIn::ConstPtr PointCloudInConstPtr;
    typedef pcl::PointCloud<NormalOut> NormalCloudOut;

    typedef pcl::KdTree<PointIn> KdTree;
    typedef pcl::KdTree<PointIn>::Ptr KdTreePtr; 

    protected:
      /** \brief An input point cloud describing the surface that is to be used for nearest neighbors estimation. */
      PointCloudInConstPtr surface_;

      /** \brief A pointer to the spatial search object. */
      KdTreePtr tree_;

      /** \brief The nearest neighbors search radius for each point. */
      double search_radius_;

      /** \brief The number of K nearest neighbors to use for each point. */
      //int k_;

      /** \brief Whether to use a polynomial fit. */
      bool use_polynomial_fit_;

      /** \brief The order of the polynomial to be fit. */
      int polynomial_order_;

      /** \brief How 'flat' should the neighbor weighting gaussian be (the smaller, the more local the fit). */
      double gaussian_parameter_;

      // ROS nodelet attributes
      /** \brief The surface PointCloud subscriber filter. */
      message_filters::Subscriber<PointCloudIn> sub_surface_filter_;
      
      /** \brief Parameter for the spatial locator tree. By convention, the values represent:
        * 0: ANN (Approximate Nearest Neigbor library) kd-tree
        * 1: FLANN (Fast Library for Approximate Nearest Neighbors) kd-tree
        * 2: Organized spatial dataset index
        */
      int spatial_locator_type_;
    
      /** \brief Pointer to a dynamic reconfigure service. */
      boost::shared_ptr <dynamic_reconfigure::Server<MLSConfig> > srv_;

      /** \brief Dynamic reconfigure callback
        * \param config the config object  
        * \param level the dynamic reconfigure level
        */
      void config_callback (MLSConfig &config, uint32_t level); 
    
    private:
      /** \brief Nodelet initialization routine. */
      virtual void onInit ();

      /** \brief Input point cloud callback.
        * \param cloud the pointer to the input point cloud
        * \param indices the pointer to the input point cloud indices
        */
      void input_indices_callback (const PointCloudInConstPtr &cloud, 
                                   const PointIndicesConstPtr &indices);


    private:
      /** \brief The PCL implementation used. */
      pcl::MovingLeastSquares<PointIn, NormalOut> impl_;
      
      /** \brief The input PointCloud subscriber. */
      ros::Subscriber sub_input_;

      /** \brief The output PointCloud (containing the normals) publisher. */
      ros::Publisher pub_normals_;

      /** \brief Synchronized input, and indices.*/
      boost::shared_ptr<message_filters::Synchronizer<sync_policies::ExactTime<PointCloudIn, PointIndices> > >       sync_input_indices_e_;
      boost::shared_ptr<message_filters::Synchronizer<sync_policies::ApproximateTime<PointCloudIn, PointIndices> > > sync_input_indices_a_;

    public:
      EIGEN_MAKE_ALIGNED_OPERATOR_NEW
  };
}

#endif  //#ifndef PCL_ROS_MOVING_LEAST_SQUARES_H_
