agv_pro_ros2/slam_gmapping/openslam_gmapping/utils/optimizer.h

#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