X-lanni
427 lines
13 KiB
C++
427 lines
13 KiB
C++
#include <list>
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#include <iostream>
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#include <gmapping/scanmatcher/scanmatcher.h>
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#include "gridlinetraversal.h"
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//#define GENERATE_MAPS
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using namespace std;
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namespace GMapping {
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const double ScanMatcher::nullLikelihood=-1.;
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ScanMatcher::ScanMatcher(): m_laserPose(0,0,0){
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m_laserAngles=0;
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m_laserBeams=0;
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m_optRecursiveIterations=3;
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m_activeAreaComputed=false;
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// This are the dafault settings for a grid map of 5 cm
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m_llsamplerange=0.01;
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m_llsamplestep=0.01;
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m_lasamplerange=0.005;
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m_lasamplestep=0.005;
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/*
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// This are the dafault settings for a grid map of 10 cm
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m_llsamplerange=0.1;
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m_llsamplestep=0.1;
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m_lasamplerange=0.02;
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m_lasamplestep=0.01;
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*/
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// This are the dafault settings for a grid map of 20/25 cm
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/*
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m_llsamplerange=0.2;
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m_llsamplestep=0.1;
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m_lasamplerange=0.02;
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m_lasamplestep=0.01;
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m_generateMap=false;
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*/
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}
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ScanMatcher::~ScanMatcher(){
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if (m_laserAngles)
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delete [] m_laserAngles;
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}
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void ScanMatcher::invalidateActiveArea(){
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m_activeAreaComputed=false;
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}
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void ScanMatcher::computeActiveArea(ScanMatcherMap& map, const OrientedPoint& p, const double* readings){
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if (m_activeAreaComputed)
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return;
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HierarchicalArray2D<PointAccumulator>::PointSet activeArea;
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OrientedPoint lp=p;
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lp.x+=cos(p.theta)*m_laserPose.x-sin(p.theta)*m_laserPose.y;
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lp.y+=sin(p.theta)*m_laserPose.x+cos(p.theta)*m_laserPose.y;
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lp.theta+=m_laserPose.theta;
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IntPoint p0=map.world2map(lp);
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const double * angle=m_laserAngles;
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for (const double* r=readings; r<readings+m_laserBeams; r++, angle++)
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if (m_generateMap){
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double d=*r;
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if (d>m_laserMaxRange)
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continue;
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if (d>m_usableRange)
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d=m_usableRange;
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Point phit=lp+Point(d*cos(lp.theta+*angle),d*sin(lp.theta+*angle));
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IntPoint p1=map.world2map(phit);
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d+=map.getDelta();
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//Point phit2=lp+Point(d*cos(lp.theta+*angle),d*sin(lp.theta+*angle));
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//IntPoint p2=map.world2map(phit2);
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IntPoint linePoints[20000] ;
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GridLineTraversalLine line;
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line.points=linePoints;
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//GridLineTraversal::gridLine(p0, p2, &line);
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GridLineTraversal::gridLine(p0, p1, &line);
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for (int i=0; i<line.num_points-1; i++){
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activeArea.insert(map.storage().patchIndexes(linePoints[i]));
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}
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if (d<=m_usableRange){
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activeArea.insert(map.storage().patchIndexes(p1));
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//activeArea.insert(map.storage().patchIndexes(p2));
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}
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} else {
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if (*r>m_laserMaxRange||*r>m_usableRange) continue;
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Point phit=lp;
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phit.x+=*r*cos(lp.theta+*angle);
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phit.y+=*r*sin(lp.theta+*angle);
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IntPoint p1=map.world2map(phit);
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assert(p1.x>=0 && p1.y>=0);
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IntPoint cp=map.storage().patchIndexes(p1);
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assert(cp.x>=0 && cp.y>=0);
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activeArea.insert(cp);
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}
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//this allocates the unallocated cells in the active area of the map
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//cout << "activeArea::size() " << activeArea.size() << endl;
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map.storage().setActiveArea(activeArea, true);
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m_activeAreaComputed=true;
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}
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void ScanMatcher::registerScan(ScanMatcherMap& map, const OrientedPoint& p, const double* readings){
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if (!m_activeAreaComputed)
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computeActiveArea(map, p, readings);
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//this operation replicates the cells that will be changed in the registration operation
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map.storage().allocActiveArea();
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OrientedPoint lp=p;
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lp.x+=cos(p.theta)*m_laserPose.x-sin(p.theta)*m_laserPose.y;
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lp.y+=sin(p.theta)*m_laserPose.x+cos(p.theta)*m_laserPose.y;
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lp.theta+=m_laserPose.theta;
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IntPoint p0=map.world2map(lp);
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const double * angle=m_laserAngles;
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for (const double* r=readings; r<readings+m_laserBeams; r++, angle++)
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if (m_generateMap){
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double d=*r;
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if (d>m_laserMaxRange)
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continue;
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if (d>m_usableRange)
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d=m_usableRange;
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Point phit=lp+Point(d*cos(lp.theta+*angle),d*sin(lp.theta+*angle));
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IntPoint p1=map.world2map(phit);
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d+=map.getDelta();
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//Point phit2=lp+Point(d*cos(lp.theta+*angle),d*sin(lp.theta+*angle));
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//IntPoint p2=map.world2map(phit2);
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IntPoint linePoints[20000] ;
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GridLineTraversalLine line;
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line.points=linePoints;
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//GridLineTraversal::gridLine(p0, p2, &line);
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GridLineTraversal::gridLine(p0, p1, &line);
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for (int i=0; i<line.num_points-1; i++){
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map.cell(line.points[i]).update(false, Point(0,0));
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}
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if (d<=m_usableRange){
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map.cell(p1).update(true,phit);
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// map.cell(p2).update(true,phit);
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}
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} else {
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if (*r>m_laserMaxRange||*r>m_usableRange) continue;
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Point phit=lp;
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phit.x+=*r*cos(lp.theta+*angle);
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phit.y+=*r*sin(lp.theta+*angle);
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map.cell(phit).update(true,phit);
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}
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}
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double ScanMatcher::optimize(OrientedPoint& pnew, const ScanMatcherMap& map, const OrientedPoint& init, const double* readings) const{
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double bestScore=-1;
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OrientedPoint currentPose=init;
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double currentScore=score(map, currentPose, readings);
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double adelta=m_optAngularDelta, ldelta=m_optLinearDelta;
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unsigned int refinement=0;
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enum Move{Front, Back, Left, Right, TurnLeft, TurnRight, Done};
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int c_iterations=0;
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do{
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if (bestScore>=currentScore){
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refinement++;
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adelta*=.5;
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ldelta*=.5;
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}
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bestScore=currentScore;
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// cout <<"score="<< currentScore << " refinement=" << refinement;
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// cout << "pose=" << currentPose.x << " " << currentPose.y << " " << currentPose.theta << endl;
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OrientedPoint bestLocalPose=currentPose;
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OrientedPoint localPose=currentPose;
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Move move=Front;
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do {
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localPose=currentPose;
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switch(move){
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case Front:
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localPose.x+=ldelta;
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move=Back;
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break;
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case Back:
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localPose.x-=ldelta;
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move=Left;
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break;
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case Left:
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localPose.y-=ldelta;
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move=Right;
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break;
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case Right:
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localPose.y+=ldelta;
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move=TurnLeft;
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break;
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case TurnLeft:
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localPose.theta+=adelta;
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move=TurnRight;
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break;
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case TurnRight:
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localPose.theta-=adelta;
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move=Done;
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break;
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default:;
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}
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double localScore=score(map, localPose, readings);
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if (localScore>currentScore){
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currentScore=localScore;
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bestLocalPose=localPose;
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}
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c_iterations++;
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} while(move!=Done);
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currentPose=bestLocalPose;
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//cout << __PRETTY_FUNCTION__ << "currentScore=" << currentScore<< endl;
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//here we look for the best move;
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}while (currentScore>bestScore || refinement<m_optRecursiveIterations);
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//cout << __PRETTY_FUNCTION__ << "bestScore=" << bestScore<< endl;
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//cout << __PRETTY_FUNCTION__ << "iterations=" << c_iterations<< endl;
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pnew=currentPose;
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return bestScore;
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}
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struct ScoredMove{
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OrientedPoint pose;
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double score;
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double likelihood;
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};
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typedef std::list<ScoredMove> ScoredMoveList;
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double ScanMatcher::optimize(OrientedPoint& _mean, ScanMatcher::CovarianceMatrix& _cov, const ScanMatcherMap& map, const OrientedPoint& init, const double* readings) const{
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ScoredMoveList moveList;
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double bestScore=-1;
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OrientedPoint currentPose=init;
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ScoredMove sm={currentPose,0,0};
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unsigned int matched=likelihoodAndScore(sm.score, sm.likelihood, map, currentPose, readings);
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double currentScore=sm.score;
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moveList.push_back(sm);
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double adelta=m_optAngularDelta, ldelta=m_optLinearDelta;
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unsigned int refinement=0;
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enum Move{Front, Back, Left, Right, TurnLeft, TurnRight, Done};
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do{
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if (bestScore>=currentScore){
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refinement++;
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adelta*=.5;
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ldelta*=.5;
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}
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bestScore=currentScore;
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// cout <<"score="<< currentScore << " refinement=" << refinement;
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// cout << "pose=" << currentPose.x << " " << currentPose.y << " " << currentPose.theta << endl;
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OrientedPoint bestLocalPose=currentPose;
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OrientedPoint localPose=currentPose;
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Move move=Front;
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do {
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localPose=currentPose;
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switch(move){
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case Front:
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localPose.x+=ldelta;
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move=Back;
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break;
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case Back:
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localPose.x-=ldelta;
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move=Left;
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break;
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case Left:
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localPose.y-=ldelta;
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move=Right;
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break;
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case Right:
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localPose.y+=ldelta;
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move=TurnLeft;
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break;
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case TurnLeft:
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localPose.theta+=adelta;
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move=TurnRight;
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break;
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case TurnRight:
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localPose.theta-=adelta;
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move=Done;
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break;
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default:;
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}
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double localScore, localLikelihood;
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//update the score
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matched=likelihoodAndScore(localScore, localLikelihood, map, localPose, readings);
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if (localScore>currentScore){
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currentScore=localScore;
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bestLocalPose=localPose;
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}
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sm.score=localScore;
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sm.likelihood=localLikelihood;
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sm.pose=localPose;
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moveList.push_back(sm);
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//update the move list
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} while(move!=Done);
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currentPose=bestLocalPose;
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//cout << __PRETTY_FUNCTION__ << "currentScore=" << currentScore<< endl;
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//here we look for the best move;
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}while (currentScore>bestScore || refinement<m_optRecursiveIterations);
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//cout << __PRETTY_FUNCTION__ << "bestScore=" << bestScore<< endl;
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//normalize the likelihood
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double lmin=1e9;
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double lmax=-1e9;
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for (ScoredMoveList::const_iterator it=moveList.begin(); it!=moveList.end(); it++){
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lmin=it->likelihood<lmin?it->likelihood:lmin;
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lmax=it->likelihood>lmax?it->likelihood:lmax;
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}
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//cout << "lmin=" << lmin << " lmax=" << lmax<< endl;
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for (ScoredMoveList::iterator it=moveList.begin(); it!=moveList.end(); it++){
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it->likelihood=exp(it->likelihood-lmax);
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//cout << "l=" << it->likelihood << endl;
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}
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//compute the mean
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OrientedPoint mean(0,0,0);
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double lacc=0;
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for (ScoredMoveList::const_iterator it=moveList.begin(); it!=moveList.end(); it++){
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mean=mean+it->pose*it->likelihood;
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lacc+=it->likelihood;
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}
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mean=mean*(1./lacc);
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//OrientedPoint delta=mean-currentPose;
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//cout << "delta.x=" << delta.x << " delta.y=" << delta.y << " delta.theta=" << delta.theta << endl;
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CovarianceMatrix cov={0.,0.,0.,0.,0.,0.};
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for (ScoredMoveList::const_iterator it=moveList.begin(); it!=moveList.end(); it++){
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OrientedPoint delta=it->pose-mean;
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delta.theta=atan2(sin(delta.theta), cos(delta.theta));
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cov.xx+=delta.x*delta.x*it->likelihood;
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cov.yy+=delta.y*delta.y*it->likelihood;
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cov.tt+=delta.theta*delta.theta*it->likelihood;
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cov.xy+=delta.x*delta.y*it->likelihood;
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cov.xt+=delta.x*delta.theta*it->likelihood;
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cov.yt+=delta.y*delta.theta*it->likelihood;
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}
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cov.xx/=lacc, cov.xy/=lacc, cov.xt/=lacc, cov.yy/=lacc, cov.yt/=lacc, cov.tt/=lacc;
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_mean=currentPose;
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_cov=cov;
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return bestScore;
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}
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void ScanMatcher::setLaserParameters
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(unsigned int beams, double* angles, const OrientedPoint& lpose){
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if (m_laserAngles)
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delete [] m_laserAngles;
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m_laserPose=lpose;
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m_laserBeams=beams;
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m_laserAngles=new double[beams];
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memcpy(m_laserAngles, angles, sizeof(double)*m_laserBeams);
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}
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double ScanMatcher::likelihood
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(double& _lmax, OrientedPoint& _mean, CovarianceMatrix& _cov, const ScanMatcherMap& map, const OrientedPoint& p, const double* readings){
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ScoredMoveList moveList;
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for (double xx=-m_llsamplerange; xx<=m_llsamplerange; xx+=m_llsamplestep)
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for (double yy=-m_llsamplerange; yy<=m_llsamplerange; yy+=m_llsamplestep)
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for (double tt=-m_lasamplerange; tt<=m_lasamplerange; tt+=m_lasamplestep){
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OrientedPoint rp=p;
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rp.x+=xx;
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rp.y+=yy;
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rp.theta+=tt;
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ScoredMove sm;
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sm.pose=rp;
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likelihoodAndScore(sm.score, sm.likelihood, map, rp, readings);
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moveList.push_back(sm);
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}
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//OrientedPoint delta=mean-currentPose;
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//cout << "delta.x=" << delta.x << " delta.y=" << delta.y << " delta.theta=" << delta.theta << endl;
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//normalize the likelihood
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double lmax=-1e9;
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double lcum=0;
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for (ScoredMoveList::const_iterator it=moveList.begin(); it!=moveList.end(); it++){
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lmax=it->likelihood>lmax?it->likelihood:lmax;
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}
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for (ScoredMoveList::iterator it=moveList.begin(); it!=moveList.end(); it++){
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//it->likelihood=exp(it->likelihood-lmax);
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lcum+=exp(it->likelihood-lmax);
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it->likelihood=exp(it->likelihood-lmax);
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//cout << "l=" << it->likelihood << endl;
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}
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OrientedPoint mean(0,0,0);
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for (ScoredMoveList::const_iterator it=moveList.begin(); it!=moveList.end(); it++){
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mean=mean+it->pose*it->likelihood;
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}
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mean=mean*(1./lcum);
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CovarianceMatrix cov={0.,0.,0.,0.,0.,0.};
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for (ScoredMoveList::const_iterator it=moveList.begin(); it!=moveList.end(); it++){
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OrientedPoint delta=it->pose-mean;
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delta.theta=atan2(sin(delta.theta), cos(delta.theta));
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cov.xx+=delta.x*delta.x*it->likelihood;
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cov.yy+=delta.y*delta.y*it->likelihood;
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cov.tt+=delta.theta*delta.theta*it->likelihood;
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cov.xy+=delta.x*delta.y*it->likelihood;
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cov.xt+=delta.x*delta.theta*it->likelihood;
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cov.yt+=delta.y*delta.theta*it->likelihood;
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}
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cov.xx/=lcum, cov.xy/=lcum, cov.xt/=lcum, cov.yy/=lcum, cov.yt/=lcum, cov.tt/=lcum;
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_mean=mean;
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_cov=cov;
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_lmax=lmax;
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return log(lcum)+lmax;
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}
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void ScanMatcher::setMatchingParameters
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(double urange, double range, double sigma, int kernsize, double lopt, double aopt, int iterations, double likelihoodSigma, unsigned int likelihoodSkip){
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m_usableRange=urange;
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m_laserMaxRange=range;
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m_kernelSize=kernsize;
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m_optLinearDelta=lopt;
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m_optAngularDelta=aopt;
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m_optRecursiveIterations=iterations;
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m_gaussianSigma=sigma;
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m_likelihoodSigma=likelihoodSigma;
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m_likelihoodSkip=likelihoodSkip;
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}
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};
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