optimizer.h

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#ifndef _OPTIMIZER_H_
#define _OPTIMIZER_H_

#include "point.h"

namespace GMapping {

struct OptimizerParams{
        double discretization;
        double angularStep, linearStep;
        int iterations;
        double maxRange;
};

template <typename Likelihood, typename Map>
struct Optimizer {
        Optimizer(const OptimizerParams& params);
        OptimizerParams params;
        Map lmap;
        Likelihood likelihood;
        OrientedPoint gradientDescent(const RangeReading& oldReading, const RangeReading& newReading);
        OrientedPoint gradientDescent(const RangeReading& oldReading, const OrientedPoint& pose, OLocalMap& Map);
        enum Move {Forward, Backward, Left, Right, TurnRight, TurnLeft};
};

template <typename Likelihood, typename Map>
Optimizer<Likelihood, Map>::Optimizer(const OptimizerParams& p):
        params(p),
        lmap(p.discretization){}

template <typename Likelihood, typename Map>
OrientedPoint Optimizer<Likelihood, Map>::gradientDescent(const RangeReading& oldReading, const RangeReading& newReading){
        lmap.clear();
        lmap.update(oldReading, OrientedPoint(0,0,0), params.maxRange);
        OrientedPoint delta=absoluteDifference(newReading.getPose(), oldReading.getPose());
        OrientedPoint bestPose=delta;
        double bestScore=likelihood(lmap, newReading, bestPose, params.maxRange);
        int it=0;
        double lstep=params.linearStep, astep=params.angularStep;
        bool increase;
/*      cerr << "bestScore=" << bestScore << endl;;*/
        do {
                increase=false;
                OrientedPoint itBestPose=bestPose;
                double itBestScore=bestScore;
                bool itIncrease;
                do {
                        itIncrease=false;
                        OrientedPoint testBestPose=itBestPose;
                        double testBestScore=itBestScore;
                        for (Move move=Forward; move<=TurnLeft; move=(Move)((int)move+1)){
                                OrientedPoint testPose=itBestPose;
                                switch(move){
                                        case Forward: testPose.x+=lstep;
                                                break;
                                        case Backward: testPose.x-=lstep;
                                                break;
                                        case Left:    testPose.y+=lstep;
                                                break;
                                        case Right:   testPose.y-=lstep;
                                                break;
                                        case TurnRight: testPose.theta-=astep;
                                                break;
                                        case TurnLeft: testPose.theta+=astep;
                                                break;
                                }
                                double score=likelihood(lmap, newReading, testPose, params.maxRange);
                                if (score>testBestScore){
                                        testBestScore=score;
                                        testBestPose=testPose;
                                }
                        }
                        if (testBestScore > itBestScore){
                                itBestScore=testBestScore;
                                itBestPose=testBestPose;
/*                              cerr << "s=" << itBestScore << " ";*/
                                itIncrease=true;
                        }
                } while(itIncrease);
                if (itBestScore > bestScore){
/*                      cerr << "S(" << itBestScore << "," <<  bestScore<< ")";*/
                        bestScore=itBestScore;
                        bestPose=itBestPose;
                        increase=true;
                } else {
                        it++;
                        lstep*=0.5;
                        astep*=0.5;
                }
        } while (it<params.iterations);
/*      cerr << "FinalBestScore" << bestScore << endl;*/
        cerr  << endl;
        return bestPose;
}

template <typename Likelihood, typename Map>
OrientedPoint Optimizer<Likelihood, Map>::gradientDescent(const RangeReading& reading, const OrientedPoint& pose, OLocalMap& lmap){
        OrientedPoint bestPose=pose;
        double bestScore=likelihood(lmap, reading, bestPose, params.maxRange);
        int it=0;
        double lstep=params.linearStep, astep=params.angularStep;
        bool increase;
/*      cerr << "bestScore=" << bestScore << endl;;*/
        do {
                increase=false;
                OrientedPoint itBestPose=bestPose;
                double itBestScore=bestScore;
                bool itIncrease;
                do {
                        itIncrease=false;
                        OrientedPoint testBestPose=itBestPose;
                        double testBestScore=itBestScore;
                        for (Move move=Forward; move<=TurnLeft; move=(Move)((int)move+1)){
                                OrientedPoint testPose=itBestPose;
                                switch(move){
                                        case Forward: testPose.x+=lstep;
                                                break;
                                        case Backward: testPose.x-=lstep;
                                                break;
                                        case Left:    testPose.y+=lstep;
                                                break;
                                        case Right:   testPose.y-=lstep;
                                                break;
                                        case TurnRight: testPose.theta-=astep;
                                                break;
                                        case TurnLeft: testPose.theta+=astep;
                                                break;
                                }
                                double score=likelihood(lmap, reading, testPose, params.maxRange);
                                if (score>testBestScore){
                                        testBestScore=score;
                                        testBestPose=testPose;
                                }
                        }
                        if (testBestScore > itBestScore){
                                itBestScore=testBestScore;
                                itBestPose=testBestPose;
/*                              cerr << "s=" << itBestScore << " ";*/
                                itIncrease=true;
                        }
                } while(itIncrease);
                if (itBestScore > bestScore){
/*                      cerr << "S(" << itBestScore << "," <<  bestScore<< ")";*/
                        bestScore=itBestScore;
                        bestPose=itBestPose;
                        increase=true;
                } else {
                        it++;
                        lstep*=0.5;
                        astep*=0.5;
                }
        } while (it<params.iterations);
/*      cerr << "FinalBestScore" << bestScore << endl;*/
        cerr  << endl;
        return bestPose;
}

} // end namespace GMapping
#endif

---

gmapping
Author(s): Giorgio Grisetti, Cyrill Stachniss, Wolfram Burgard; ROS wrapper by Brian Gerkey
autogenerated on Fri Jan 11 09:32:07 2013