SelfLocalizer.h

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#ifndef SELF_LOCALIZER_H
#define SELF_LOCALIZER_H
 
#include <string>
 
#include <ros/ros.h>
#include <tf/transform_listener.h>
#include <nav_msgs/OccupancyGrid.h>
#include <geometry_msgs/PoseArray.h>
#include <sensor_msgs/LaserScan.h>
 
#include <nav2d_localizer/map.h>
#include <nav2d_localizer/pf.h>
#include <nav2d_localizer/pf_pdf.h>
 
class OdometryData
{
public:
        OdometryData(const tf::StampedTransform& pNewPose, const tf::StampedTransform& pLastPose);
        
        double mDeltaX;
        double mDeltaY;
        double mDeltaTheta;
};
 
class LaserData
{
public:
        LaserData(const sensor_msgs::LaserScan::ConstPtr& scan);
        ~LaserData();
        
        int mRangeCount;
        double mRangeMax;
        double (*mRanges)[2];
};
 
class SelfLocalizer
{
public:
        SelfLocalizer(bool publish = true);
        ~SelfLocalizer();
        
        bool initialize();
        
        // Uniformly distribute particles in the map
        static pf_vector_t distributeParticles(void* map);
        
        // Create a map from a ROS OccupancyGrid
        void convertMap(const nav_msgs::OccupancyGrid& map_msg);
        
        void process(const sensor_msgs::LaserScan::ConstPtr& scan);
        
        double getCovariance();
        tf::Transform getBestPose();
 
        tf::StampedTransform getMapToOdometry()
        {
                return tf::StampedTransform(mMapToOdometry, ros::Time::now(), mMapFrame, mOdometryFrame);
        }
        
        void publishParticleCloud();
        
        // These methods are static, so the particle filter can call them via callback.
        // The parameters are static, so they can be used within these two methods.
        static double calculateMoveModel(OdometryData* data, pf_sample_set_t* set);
        static double calculateBeamModel(LaserData *data, pf_sample_set_t* set);
        static double calculateLikelihoodFieldModel(LaserData *data, pf_sample_set_t* set);
 
private:        
        // Parameters for movement model (I don't know what they do.)
        static double sAlpha1, sAlpha2, sAlpha3, sAlpha4;
        static pf_vector_t sLaserPose;
        
        // How many range readings from every scan are used for the sensor model update
        static int sMaxBeams;
        
        // Max distance at which we care about obstacles for constructing the likelihood field
        static double sLikelihoodMaxDist;
        
        // The map
        static map_t* sMap;
        
        // Stddev of Gaussian model for laser hits
        static double sSigmaHit;
        
        // Decay rate of exponential model for short readings
        static double sLamdaShort;
        
        // Mixture params for the components of the model, must sum to 1
        static double sZHit;
        static double sZShort;
        static double sZMax;
        static double sZRand;
        
        std::string mMapFrame;
        std::string mOdometryFrame;
        std::string mRobotFrame;
        std::string mLaserFrame;
        
private:
        // The actual particle filter
        pf_t* mParticleFilter;
        int mProcessedScans;
        
        // ROS stuff
        tf::TransformListener mTransformListener;
        ros::Publisher mParticlePublisher;
        
        // The pose of the last scan used for localization
        static tf::StampedTransform mLastPose;
        
        // The current localization result
        tf::Transform mMapToOdometry;
        
        // Parameters
        int mMinParticles;
        int mMaxParticles;
        double mAlphaSlow;
        double mAlphaFast;
        double mPopulationErr;
        double mPopulationZ;    
        
        double mMinTranslation;
        double mMinRotation;
        
        bool mPublishParticles;
        bool mFirstScanReceived;
        
        int mLaserModelType; // 1 = beam, 2 = likelihood field
        
        pf_sensor_model_fn_t mSensorModelFunction;
        pf_action_model_fn_t mActionModelFunction;
};
 
#endif