bool ScanMatcher::globalMatching(OptimizableGraph::VertexSet& referenceVset, OptimizableGraph::Vertex* _referenceVertex, OptimizableGraph::Vertex* _currentVertex, SE2 *trel, double maxScore){
OptimizableGraph::VertexSet vset;
vset.insert(_currentVertex);
return globalMatching(referenceVset, _referenceVertex, vset, _currentVertex, trel, maxScore);
}
bool ScanMatcher::scanMatchingLC(OptimizableGraph::VertexSet& referenceVset, OptimizableGraph::Vertex* _referenceVertex, OptimizableGraph::Vertex* _currentVertex, std::vector<SE2>& trel, double maxScore){
OptimizableGraph::VertexSet currvset;
currvset.insert(_currentVertex);
return scanMatchingLC(referenceVset, _referenceVertex, currvset, _currentVertex, trel, maxScore);
//return scanMatchingLChierarchical(referenceVset, _originVertex, currvset, _currentVertex, trel, maxScore);
}
void GraphSLAM::checkHaveLaser(OptimizableGraph::VertexSet& vset){
OptimizableGraph::VertexSet tmpvset = vset;
for (OptimizableGraph::VertexSet::iterator it = tmpvset.begin(); it != tmpvset.end(); it++){
OptimizableGraph::Vertex *vertex = (OptimizableGraph::Vertex*) *it;
if (!findLaserData(vertex))
vset.erase(*it);
}
}
void EdgePointXYCovPointXYCov::initialEstimate(const OptimizableGraph::VertexSet& from, OptimizableGraph::Vertex* to)
{
assert(from.size() == 1 && from.count(_vertices[0]) == 1 && "Can not initialize VertexSE2 position by VertexPointXYCov");
VertexPointXYCov* vi = static_cast<VertexPointXYCov*>(_vertices[0]);
VertexPointXYCov* vj = static_cast<VertexPointXYCov*>(_vertices[1]);
if (from.count(vi) > 0 && to == vj) {
vj->estimate() = vi->estimate(); // * _measurement;
}
}
void EstimatePropagator::propagate(OptimizableGraph::Vertex* v,
const EstimatePropagator::PropagateCost& cost,
const EstimatePropagator::PropagateAction& action,
double maxDistance,
double maxEdgeCost)
{
OptimizableGraph::VertexSet vset;
vset.insert(v);
propagate(vset, cost, action, maxDistance, maxEdgeCost);
}
void EdgeSE2PointXYCalib::initialEstimate(const OptimizableGraph::VertexSet& from, OptimizableGraph::Vertex* /*to*/)
{
assert(from.size() == 1 && from.count(_vertices[0]) == 1 && "Can not initialize VertexSE2 position by VertexPointXY");
if (from.count(_vertices[0]) != 1)
return;
VertexSE2* vi = static_cast<VertexSE2*>(_vertices[0]);
VertexPointXY* vj = static_cast<VertexPointXY*>(_vertices[1]);
vj->setEstimate(vi->estimate() * _measurement);
}
void GraphSLAM::addNeighboringVertices(OptimizableGraph::VertexSet& vset, int gap){
OptimizableGraph::VertexSet temp = vset;
for (OptimizableGraph::VertexSet::iterator it = temp.begin(); it!=temp.end(); it++){
OptimizableGraph::Vertex* vertex = (OptimizableGraph::Vertex*) *it;
for (int i = 1; i <= gap; i++){
OptimizableGraph::Vertex *v = (OptimizableGraph::Vertex *) _graph->vertex(vertex->id()+i);
if (v && v->id() != _lastVertex->id()){
OptimizableGraph::VertexSet::iterator itv = vset.find(v);
if (itv == vset.end())
vset.insert(v);
else
break;
}
}
for (int i = 1; i <= gap; i++){
OptimizableGraph::Vertex* v = (OptimizableGraph::Vertex*) _graph->vertex(vertex->id()-i);
if (v && v->id() != _lastVertex->id()){
OptimizableGraph::VertexSet::iterator itv = vset.find(v);
if (itv == vset.end())
vset.insert(v);
else
break;
}
}
}
}
void EdgeSE3PointXYZDepth::initialEstimate(const OptimizableGraph::VertexSet& from, OptimizableGraph::Vertex* /*to_*/)
{
(void) from;
assert(from.size() == 1 && from.count(_vertices[0]) == 1 && "Can not initialize VertexDepthCam position by VertexTrackXYZ");
VertexSE3 *cam = dynamic_cast<VertexSE3*>(_vertices[0]);
VertexPointXYZ *point = dynamic_cast<VertexPointXYZ*>(_vertices[1]);
const Eigen::Matrix<double, 3, 3>& invKcam = params->invKcam();
Eigen::Vector3d p;
p(2) = _measurement(2);
p.head<2>() = _measurement.head<2>()*p(2);
p=invKcam*p;
point->setEstimate(cam->estimate() * (params->offsetMatrix() * p));
}
void EdgeSE2PointXYBearing::initialEstimate(const OptimizableGraph::VertexSet& from, OptimizableGraph::Vertex* /*to*/)
{
assert(from.size() == 1 && from.count(_vertices[0]) == 1 && "Can not initialize VertexSE2 position by VertexPointXY");
if (from.count(_vertices[0]) != 1)
return;
double r=2.;
const VertexSE2* v1 = static_cast<const VertexSE2*>(_vertices[0]);
VertexPointXY* l2 = static_cast<VertexPointXY*>(_vertices[1]);
SE2 t=v1->estimate();
t.setRotation(t.rotation().angle()+_measurement);
Vector2d vr;
vr[0]=r; vr[1]=0;
l2->setEstimate(t*vr);
}
void EdgeSE3PointXYZ::initialEstimate(const OptimizableGraph::VertexSet& from, OptimizableGraph::Vertex* /*to_*/)
{
(void) from;
assert(from.size() == 1 && from.count(_vertices[0]) == 1 && "Can not initialize VertexDepthCam position by VertexTrackXYZ");
VertexSE3 *cam = dynamic_cast<VertexSE3*>(_vertices[0]);
VertexPointXYZ *point = dynamic_cast<VertexPointXYZ*>(_vertices[1]);
// SE3OffsetCache* vcache = (SE3OffsetCache* ) cam->getCache(_cacheIds[0]);
// if (! vcache){
// cerr << "fatal error in retrieving cache" << endl;
// }
// SE3OffsetParameters* params=vcache->params;
Eigen::Vector3d p=_measurement;
point->setEstimate(cam->estimate() * (offsetParam->offset() * p));
}
void EdgeSE2::initialEstimate(const OptimizableGraph::VertexSet& from, OptimizableGraph::Vertex* /* to */)
{
VertexSE2* fromEdge = static_cast<VertexSE2*>(_vertices[0]);
VertexSE2* toEdge = static_cast<VertexSE2*>(_vertices[1]);
if (from.count(fromEdge) > 0)
toEdge->setEstimate(fromEdge->estimate() * _measurement);
else
fromEdge->setEstimate(toEdge->estimate() * _inverseMeasurement);
}
void GraphSLAM::checkCovariance(OptimizableGraph::VertexSet& vset){
///////////////////////////////////
// we need now to compute the marginal covariances of all other vertices w.r.t the newly inserted one
CovarianceEstimator ce(_graph);
ce.setVertices(vset);
ce.setGauge(_lastVertex);
ce.compute();
assert(!_lastVertex->fixed() && "last Vertex is fixed");
assert(_firstRobotPose->fixed() && "first Vertex is not fixed");
OptimizableGraph::VertexSet tmpvset = vset;
for (OptimizableGraph::VertexSet::iterator it = tmpvset.begin(); it != tmpvset.end(); it++){
VertexSE2 *vertex = (VertexSE2*) *it;
MatrixXd Pv = ce.getCovariance(vertex);
Matrix2d Pxy; Pxy << Pv(0,0), Pv(0,1), Pv(1,0), Pv(1,1);
SE2 delta = vertex->estimate().inverse() * _lastVertex->estimate();
Vector2d hxy (delta.translation().x(), delta.translation().y());
double perceptionRange =1;
if (hxy.x()-perceptionRange>0)
hxy.x() -= perceptionRange;
else if (hxy.x()+perceptionRange<0)
hxy.x() += perceptionRange;
else
hxy.x() = 0;
if (hxy.y()-perceptionRange>0)
hxy.y() -= perceptionRange;
else if (hxy.y()+perceptionRange<0)
hxy.y() += perceptionRange;
else
hxy.y() = 0;
double d2 = hxy.transpose() * Pxy.inverse() * hxy;
if (d2 > 5.99)
vset.erase(*it);
}
}
void EdgeSE3::initialEstimate(const OptimizableGraph::VertexSet& from_, OptimizableGraph::Vertex* /*to_*/) {
VertexSE3 *from = static_cast<VertexSE3*>(_vertices[0]);
VertexSE3 *to = static_cast<VertexSE3*>(_vertices[1]);
if (from_.count(from) > 0) {
to->setEstimate(from->estimate() * _measurement);
} else
from->setEstimate(to->estimate() * _measurement.inverse());
//cerr << "IE" << endl;
}
void EdgeSE2PlaceVicinity::initialEstimate(
const OptimizableGraph::VertexSet& from,
OptimizableGraph::Vertex* /*to*/) {
VertexSE2* fromEdge = static_cast<VertexSE2*>(_vertices[0]);
VertexSE2* toEdge = static_cast<VertexSE2*>(_vertices[1]);
if (from.count(fromEdge) > 0)
toEdge->setEstimate(fromEdge->estimate());
else
fromEdge->setEstimate(toEdge->estimate());
}
void EdgeSE3Offset::initialEstimate(const OptimizableGraph::VertexSet& from_, OptimizableGraph::Vertex* /*to_*/) {
VertexSE3 *from = static_cast<VertexSE3*>(_vertices[0]);
VertexSE3 *to = static_cast<VertexSE3*>(_vertices[1]);
Eigen::Isometry3d virtualMeasurement = _cacheFrom->offsetParam()->offset() * measurement() * _cacheTo->offsetParam()->offset().inverse();
if (from_.count(from) > 0) {
to->setEstimate(from->estimate() * virtualMeasurement);
} else
from->setEstimate(to->estimate() * virtualMeasurement.inverse());
}
void EdgeSE3PointXYZDisparity::initialEstimate(const OptimizableGraph::VertexSet& from, OptimizableGraph::Vertex* /*to*/)
{
(void) from;
assert(from.size() == 1 && from.count(_vertices[0]) == 1 && "Can not initialize VertexDepthCam position by VertexTrackXYZ");
VertexSE3 *cam = dynamic_cast<VertexSE3*>(_vertices[0]);
VertexPointXYZ *point = dynamic_cast<VertexPointXYZ*>(_vertices[1]);
// VertexCameraCache* vcache = (VertexCameraCache* ) cam->getCache(_cacheIds[0]);
// if (! vcache){
// cerr << "fatal error in retrieving cache" << endl;
// }
//ParameterCamera* params=vcache->params;
const Eigen::Matrix<double, 3, 3>& invKcam = params->invKcam();
Eigen::Vector3d p;
double w=1./_measurement(2);
p.head<2>() = _measurement.head<2>()*w;
p(2) = w;
p = invKcam * p;
p = cam->estimate() * (params->offset() * p);
point->setEstimate(p);
}
double EstimatePropagatorCostOdometry::operator()(OptimizableGraph::Edge* edge, const OptimizableGraph::VertexSet& from_, OptimizableGraph::Vertex* to_) const
{
OptimizableGraph::Edge* e = dynamic_cast<OptimizableGraph::Edge*>(edge);
OptimizableGraph::Vertex* from = dynamic_cast<OptimizableGraph::Vertex*>(*from_.begin());
OptimizableGraph::Vertex* to = dynamic_cast<OptimizableGraph::Vertex*>(to_);
if (std::abs(from->id() - to->id()) != 1) // simple method to identify odometry edges in a pose graph
return std::numeric_limits<double>::max();
SparseOptimizer::EdgeContainer::const_iterator it = _graph->findActiveEdge(e);
if (it == _graph->activeEdges().end()) // it has to be an active edge
return std::numeric_limits<double>::max();
return e->initialEstimatePossible(from_, to);
}
void transformPointsFromVSet(OptimizableGraph::VertexSet& vset, OptimizableGraph::Vertex* _referenceVertex, RawLaser::Point2DVector& scansInRefVertex){
VertexSE2* referenceVertex=dynamic_cast<VertexSE2*>(_referenceVertex);
scansInRefVertex.clear();
for (OptimizableGraph::VertexSet::iterator it = vset.begin(); it != vset.end(); it++){
VertexSE2 *vertex = (VertexSE2*) *it;
RobotLaser* laserv = dynamic_cast<RobotLaser*>(vertex->userData());
if (laserv){
RawLaser::Point2DVector vscan = laserv->cartesian();
SE2 trl = laserv->laserParams().laserPose;
RawLaser::Point2DVector scanInRefVertex;
if (vertex->id() == referenceVertex->id()){
ScanMatcher::applyTransfToScan(trl, vscan, scanInRefVertex);
}else{
SE2 trel = referenceVertex->estimate().inverse() * vertex->estimate();
SE2 transf = trel * trl;
ScanMatcher::applyTransfToScan(transf, vscan, scanInRefVertex);
}
scansInRefVertex.insert(scansInRefVertex.end(), scanInRefVertex.begin(), scanInRefVertex.end());
}
}
}
void EdgeSE2DistanceOrientation::initialEstimate(
const OptimizableGraph::VertexSet& from,
OptimizableGraph::Vertex* /*to*/) {
VertexSE2* fromEdge = static_cast<VertexSE2*>(_vertices[0]);
VertexSE2* toEdge = static_cast<VertexSE2*>(_vertices[1]);
double dist = sqrt(_measurement[0] * _measurement[0] + _measurement[1] * _measurement[1]);
double theta = _measurement[2];
double x = dist * cos(theta), y = dist * sin(theta);
SE2 tmpMeasurement(x,y,theta);
SE2 inverseTmpMeasurement = tmpMeasurement.inverse();
if (from.count(fromEdge) > 0)
toEdge->setEstimate(fromEdge->estimate() * tmpMeasurement);
else
fromEdge->setEstimate(toEdge->estimate() * inverseTmpMeasurement);
}
void computeSimpleStars(StarSet& stars,
SparseOptimizer* optimizer,
EdgeLabeler* labeler,
EdgeCreator* creator,
OptimizableGraph::Vertex* gauge_,
std::string edgeTag,
std::string vertexTag,
int level,
int step,
int backboneIterations,
int starIterations,
double rejectionThreshold,
bool debug){
cerr << "preforming the tree actions" << endl;
HyperDijkstra d(optimizer);
// compute a spanning tree based on the types of edges and vertices in the pool
EdgeTypesCostFunction f(edgeTag, vertexTag, level);
d.shortestPaths(gauge_,
&f,
std::numeric_limits< double >::max(),
1e-6,
false,
std::numeric_limits< double >::max()/2);
HyperDijkstra::computeTree(d.adjacencyMap());
// constructs the stars on the backbone
BackBoneTreeAction bact(optimizer, vertexTag, level, step);
bact.init();
cerr << "free edges size " << bact.freeEdges().size() << endl;
// perform breadth-first visit of the visit tree and create the stars on the backbone
d.visitAdjacencyMap(d.adjacencyMap(),&bact,true);
stars.clear();
for (VertexStarMultimap::iterator it=bact.vertexStarMultiMap().begin();
it!=bact.vertexStarMultiMap().end(); it++){
stars.insert(it->second);
}
cerr << "stars.size: " << stars.size() << endl;
cerr << "size: " << bact.vertexStarMultiMap().size() << endl;
// for each star
// for all vertices in the backbone, select all edges leading/leaving from that vertex
// that are contained in freeEdges.
// mark the corresponding "open" vertices and add them to a multimap (vertex->star)
// select a gauge in the backbone
// push all vertices on the backbone
// compute an initial guess on the backbone
// one round of optimization backbone
// lock all vertices in the backbone
// push all "open" vertices
// for each open vertex,
// compute an initial guess given the backbone
// do some rounds of solveDirect
// if (fail)
// - remove the vertex and the edges in that vertex from the star
// - make the structures consistent
// pop all "open" vertices
// pop all "vertices" in the backbone
// unfix the vertices in the backbone
int starNum=0;
for (StarSet::iterator it=stars.begin(); it!=stars.end(); it++){
Star* s =*it;
HyperGraph::VertexSet backboneVertices = s->_lowLevelVertices;
HyperGraph::EdgeSet backboneEdges = s->_lowLevelEdges;
if (backboneEdges.empty())
continue;
// cerr << "optimizing backbone" << endl;
// one of these should be the gauge, to be simple we select the fisrt one in the backbone
OptimizableGraph::VertexSet gauge;
gauge.insert(*backboneVertices.begin());
s->gauge()=gauge;
s->optimizer()->push(backboneVertices);
s->optimizer()->setFixed(gauge,true);
s->optimizer()->initializeOptimization(backboneEdges);
s->optimizer()->computeInitialGuess();
s->optimizer()->optimize(backboneIterations);
s->optimizer()->setFixed(backboneVertices, true);
// cerr << "assignind edges.vertices not in bbone" << endl;
HyperGraph::EdgeSet otherEdges;
HyperGraph::VertexSet otherVertices;
std::multimap<HyperGraph::Vertex*, HyperGraph::Edge*> vemap;
for (HyperGraph::VertexSet::iterator bit=backboneVertices.begin(); bit!=backboneVertices.end(); bit++){
HyperGraph::Vertex* v=*bit;
for (HyperGraph::EdgeSet::iterator eit=v->edges().begin(); eit!=v->edges().end(); eit++){
OptimizableGraph::Edge* e = (OptimizableGraph::Edge*) *eit;
HyperGraph::EdgeSet::iterator feit=bact.freeEdges().find(e);
if (feit!=bact.freeEdges().end()){ // edge is admissible
otherEdges.insert(e);
bact.freeEdges().erase(feit);
for (size_t i=0; i<e->vertices().size(); i++){
OptimizableGraph::Vertex* ve= (OptimizableGraph::Vertex*)e->vertices()[i];
if (backboneVertices.find(ve)==backboneVertices.end()){
otherVertices.insert(ve);
vemap.insert(make_pair(ve,e));
}
}
}
}
}
// RAINER TODO maybe need a better solution than dynamic casting here??
OptimizationAlgorithmWithHessian* solverWithHessian = dynamic_cast<OptimizationAlgorithmWithHessian*>(s->optimizer()->solver());
if (solverWithHessian) {
s->optimizer()->push(otherVertices);
// cerr << "optimizing vertices out of bbone" << endl;
// cerr << "push" << endl;
// cerr << "init" << endl;
s->optimizer()->initializeOptimization(otherEdges);
// cerr << "guess" << endl;
s->optimizer()->computeInitialGuess();
// cerr << "solver init" << endl;
s->optimizer()->solver()->init();
// cerr << "structure" << endl;
if (!solverWithHessian->buildLinearStructure())
cerr << "FATAL: failure while building linear structure" << endl;
// cerr << "errors" << endl;
s->optimizer()->computeActiveErrors();
// cerr << "system" << endl;
solverWithHessian->updateLinearSystem();
// cerr << "directSolove" << endl;
} else {
cerr << "FATAL: hierarchical thing cannot be used with a solver that does not support the system structure construction" << endl;
}
// // then optimize the vertices one at a time to check if a solution is good
for (HyperGraph::VertexSet::iterator vit=otherVertices.begin(); vit!=otherVertices.end(); vit++){
OptimizableGraph::Vertex* v=(OptimizableGraph::Vertex*)(*vit);
v->solveDirect();
// cerr << " " << d;
// if a solution is found, add a vertex and all the edges in
//othervertices that are pointing to that edge to the star
s->_lowLevelVertices.insert(v);
for (HyperGraph::EdgeSet::iterator eit=v->edges().begin(); eit!=v->edges().end(); eit++){
OptimizableGraph::Edge* e = (OptimizableGraph::Edge*) *eit;
if (otherEdges.find(e)!=otherEdges.end())
s->_lowLevelEdges.insert(e);
}
}
//cerr << endl;
// relax the backbone and optimize it all
// cerr << "relax bbone" << endl;
s->optimizer()->setFixed(backboneVertices, false);
//cerr << "fox gauge bbone" << endl;
s->optimizer()->setFixed(s->gauge(),true);
//cerr << "opt init" << endl;
s->optimizer()->initializeOptimization(s->_lowLevelEdges);
optimizer->computeActiveErrors();
double initialChi = optimizer->activeChi2();
int starOptResult = s->optimizer()->optimize(starIterations);
//cerr << starOptResult << "(" << starIterations << ") " << endl;
double finalchi=-1.;
cerr << "computing star: " << starNum << endl;
int vKept=0, vDropped=0;
if (!starIterations || starOptResult > 0 ){
optimizer->computeActiveErrors();
finalchi = optimizer->activeChi2();
#if 1
s->optimizer()->computeActiveErrors();
// cerr << "system" << endl;
if (solverWithHessian)
solverWithHessian->updateLinearSystem();
HyperGraph::EdgeSet prunedStarEdges = backboneEdges;
HyperGraph::VertexSet prunedStarVertices = backboneVertices;
for (HyperGraph::VertexSet::iterator vit=otherVertices.begin(); vit!=otherVertices.end(); vit++){
//discard the vertices whose error is too big
OptimizableGraph::Vertex* v=(OptimizableGraph::Vertex*)(*vit);
MatrixXd h(v->dimension(), v->dimension());
for (int i=0; i<v->dimension(); i++){
for (int j=0; j<v->dimension(); j++)
h(i,j)=v->hessian(i,j);
}
EigenSolver<Eigen::MatrixXd> esolver;
esolver.compute(h);
VectorXcd ev= esolver.eigenvalues();
double emin = std::numeric_limits<double>::max();
double emax = -std::numeric_limits<double>::max();
for (int i=0; i<ev.size(); i++){
emin = ev(i).real()>emin ? emin : ev(i).real();
emax = ev(i).real()<emax ? emax : ev(i).real();
}
double d=emin/emax;
// cerr << " " << d;
if (d>rejectionThreshold){
// if a solution is found, add a vertex and all the edges in
//othervertices that are pointing to that edge to the star
prunedStarVertices.insert(v);
for (HyperGraph::EdgeSet::iterator eit=v->edges().begin(); eit!=v->edges().end(); eit++){
OptimizableGraph::Edge* e = (OptimizableGraph::Edge*) *eit;
if (otherEdges.find(e)!=otherEdges.end())
prunedStarEdges.insert(e);
}
//cerr << "K( " << v->id() << "," << d << ")" ;
vKept ++;
} else {
vDropped++;
//cerr << "R( " << v->id() << "," << d << ")" ;
}
}
s->_lowLevelEdges=prunedStarEdges;
s->_lowLevelVertices=prunedStarVertices;
#endif
//cerr << "addHedges" << endl;
//now add to the star the hierarchical edges
std::vector<OptimizableGraph::Vertex*> vertices(2);
vertices[0]= (OptimizableGraph::Vertex*) *s->_gauge.begin();
for (HyperGraph::VertexSet::iterator vit=s->_lowLevelVertices.begin(); vit!=s->_lowLevelVertices.end(); vit++){
OptimizableGraph::Vertex* v=(OptimizableGraph::Vertex*)*vit;
vertices[1]=v;
if (v==vertices[0])
continue;
OptimizableGraph::Edge* e=creator->createEdge(vertices);
//rr << "creating edge" << e << Factory::instance()->tag(vertices[0]) << "->" << Factory::instance()->tag(v) <endl;
if (e) {
e->setLevel(level+1);
optimizer->addEdge(e);
s->_starEdges.insert(e);
} else {
cerr << "HERE" << endl;
cerr << "FATAL, cannot create edge" << endl;
}
}
}
cerr << " gauge: " << (*s->_gauge.begin())->id()
<< " kept: " << vKept
<< " dropped: " << vDropped
<< " edges:" << s->_lowLevelEdges.size()
<< " hedges" << s->_starEdges.size()
<< " initial chi " << initialChi
<< " final chi " << finalchi << endl;
if (debug) {
char starLowName[100];
sprintf(starLowName, "star-%04d-low.g2o", starNum);
ofstream starLowStream(starLowName);
optimizer->saveSubset(starLowStream, s->_lowLevelEdges);
}
bool labelOk=false;
if (!starIterations || starOptResult > 0)
labelOk = s->labelStarEdges(0, labeler);
if (labelOk) {
if (debug) {
char starHighName[100];
sprintf(starHighName, "star-%04d-high.g2o", starNum);
ofstream starHighStream(starHighName);
optimizer->saveSubset(starHighStream, s->_starEdges);
}
} else {
cerr << "FAILURE: " << starOptResult << endl;
}
starNum++;
//label each hierarchical edge
s->optimizer()->pop(otherVertices);
s->optimizer()->pop(backboneVertices);
s->optimizer()->setFixed(s->gauge(),false);
}
StarSet stars2;
// now erase the stars that have 0 edges. They r useless
for (StarSet::iterator it=stars.begin(); it!=stars.end(); it++){
Star* s=*it;
if (s->lowLevelEdges().size()==0) {
delete s;
} else
stars2.insert(s);
}
stars=stars2;
}
void EstimatePropagator::propagate(OptimizableGraph::VertexSet& vset,
const EstimatePropagator::PropagateCost& cost,
const EstimatePropagator::PropagateAction& action,
double maxDistance,
double maxEdgeCost)
{
reset();
PriorityQueue frontier;
for (OptimizableGraph::VertexSet::iterator vit=vset.begin(); vit!=vset.end(); ++vit){
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*vit);
AdjacencyMap::iterator it = _adjacencyMap.find(v);
assert(it != _adjacencyMap.end());
it->second._distance = 0.;
it->second._parent.clear();
it->second._frontierLevel = 0;
frontier.push(&it->second);
}
while(! frontier.empty()){
AdjacencyMapEntry* entry = frontier.pop();
OptimizableGraph::Vertex* u = entry->child();
double uDistance = entry->distance();
//cerr << "uDistance " << uDistance << endl;
// initialize the vertex
if (entry->_frontierLevel > 0) {
action(entry->edge(), entry->parent(), u);
}
/* std::pair< OptimizableGraph::VertexSet::iterator, bool> insertResult = */ _visited.insert(u);
OptimizableGraph::EdgeSet::iterator et = u->edges().begin();
while (et != u->edges().end()){
OptimizableGraph::Edge* edge = static_cast<OptimizableGraph::Edge*>(*et);
++et;
int maxFrontier = -1;
OptimizableGraph::VertexSet initializedVertices;
for (size_t i = 0; i < edge->vertices().size(); ++i) {
OptimizableGraph::Vertex* z = static_cast<OptimizableGraph::Vertex*>(edge->vertex(i));
AdjacencyMap::iterator ot = _adjacencyMap.find(z);
if (ot->second._distance != numeric_limits<double>::max()) {
initializedVertices.insert(z);
maxFrontier = (max)(maxFrontier, ot->second._frontierLevel);
}
}
assert(maxFrontier >= 0);
for (size_t i = 0; i < edge->vertices().size(); ++i) {
OptimizableGraph::Vertex* z = static_cast<OptimizableGraph::Vertex*>(edge->vertex(i));
if (z == u)
continue;
size_t wasInitialized = initializedVertices.erase(z);
double edgeDistance = cost(edge, initializedVertices, z);
if (edgeDistance > 0. && edgeDistance != std::numeric_limits<double>::max() && edgeDistance < maxEdgeCost) {
double zDistance = uDistance + edgeDistance;
//cerr << z->id() << " " << zDistance << endl;
AdjacencyMap::iterator ot = _adjacencyMap.find(z);
assert(ot!=_adjacencyMap.end());
if (zDistance < ot->second.distance() && zDistance < maxDistance){
//if (ot->second.inQueue)
//cerr << "Updating" << endl;
ot->second._distance = zDistance;
ot->second._parent = initializedVertices;
ot->second._edge = edge;
ot->second._frontierLevel = maxFrontier + 1;
frontier.push(&ot->second);
}
}
if (wasInitialized > 0)
initializedVertices.insert(z);
}
}
}
// writing debug information like cost for reaching each vertex and the parent used to initialize
#ifdef DEBUG_ESTIMATE_PROPAGATOR
cerr << "Writing cost.dat" << endl;
ofstream costStream("cost.dat");
for (AdjacencyMap::const_iterator it = _adjacencyMap.begin(); it != _adjacencyMap.end(); ++it) {
HyperGraph::Vertex* u = it->second.child();
costStream << "vertex " << u->id() << " cost " << it->second._distance << endl;
}
cerr << "Writing init.dat" << endl;
ofstream initStream("init.dat");
vector<AdjacencyMapEntry*> frontierLevels;
for (AdjacencyMap::iterator it = _adjacencyMap.begin(); it != _adjacencyMap.end(); ++it) {
if (it->second._frontierLevel > 0)
frontierLevels.push_back(&it->second);
}
sort(frontierLevels.begin(), frontierLevels.end(), FrontierLevelCmp());
for (vector<AdjacencyMapEntry*>::const_iterator it = frontierLevels.begin(); it != frontierLevels.end(); ++it) {
AdjacencyMapEntry* entry = *it;
OptimizableGraph::Vertex* to = entry->child();
initStream << "calling init level = " << entry->_frontierLevel << "\t (";
for (OptimizableGraph::VertexSet::iterator pit = entry->parent().begin(); pit != entry->parent().end(); ++pit) {
initStream << " " << (*pit)->id();
}
initStream << " ) -> " << to->id() << endl;
}
#endif
}
bool ScanMatcher::scanMatchingLC(OptimizableGraph::VertexSet& referenceVset, OptimizableGraph::Vertex* _referenceVertex, OptimizableGraph::VertexSet& currvset, OptimizableGraph::Vertex* _currentVertex, std::vector<SE2>& trel, double maxScore){
cerr << "Loop Closing Scan Matching" << endl;
//cerr << "Size of Vset " << referenceVset.size() << endl;
VertexSE2* referenceVertex =dynamic_cast<VertexSE2*>(_referenceVertex);
resetGrid();
trel.clear();
RawLaser::Point2DVector scansInRefVertex;
transformPointsFromVSet(referenceVset, _referenceVertex, scansInRefVertex);
_grid.addAndConvolvePoints<RawLaser::Point2DVector>(scansInRefVertex.begin(), scansInRefVertex.end(), _kernel);
RawLaser::Point2DVector scansInCurVertex;
transformPointsFromVSet(currvset, _currentVertex, scansInCurVertex);
Vector2dVector reducedScans;
CharGrid::subsample(reducedScans, scansInCurVertex, 0.1);
RegionVector regions;
RegionVector regionspi;
for (OptimizableGraph::VertexSet::iterator it = referenceVset.begin(); it != referenceVset.end(); it++){
VertexSE2 *vertex = (VertexSE2*) *it;
Region reg;
SE2 relposv(.0, .0, .0);
if (vertex->id() != referenceVertex->id())
relposv = referenceVertex->estimate().inverse() * vertex->estimate();
Vector3f lower(-.5+relposv.translation().x(), -2.+relposv.translation().y(), -1.+relposv.rotation().angle());
Vector3f upper( .5+relposv.translation().x(), 2.+relposv.translation().y(), 1.+relposv.rotation().angle());
reg.lowerLeft = lower;
reg.upperRight = upper;
regions.push_back(reg);
lower[2] += M_PI;
upper[2] += M_PI;
reg.lowerLeft = lower;
reg.upperRight = upper;
regionspi.push_back(reg);
}
std::vector<MatcherResult> mresvec;
double thetaRes = 0.025; // was 0.0125*.5
//Results discretization
double dx = 0.5, dy = 0.5, dth = 0.2;
std::map<DiscreteTriplet, MatcherResult> resultsMap;
clock_t t_ini, t_fin;
double secs;
t_ini = clock();
_grid.greedySearch(mresvec, reducedScans, regions, thetaRes, maxScore, dx, dy, dth);
t_fin = clock();
secs = (double)(t_fin - t_ini) / CLOCKS_PER_SEC;
printf("%.16g ms. Matcher results: %i\n", secs * 1000.0, (int) mresvec.size());
if (mresvec.size()){
mresvec[0].transformation[2] = normalize_theta(mresvec[0].transformation[2]);
cerr << "Found Loop Closure Edge. Transf: " << mresvec[0].transformation.x() << " " << mresvec[0].transformation.y() << " " << mresvec[0].transformation.z() << endl;
CharGrid::addToPrunedMap(resultsMap, mresvec[0], dx, dy, dth);
}
t_ini = clock();
_grid.greedySearch(mresvec, reducedScans, regionspi, thetaRes, maxScore, dx, dy, dth);
t_fin = clock();
secs = (double)(t_fin - t_ini) / CLOCKS_PER_SEC;
printf("%.16g ms. Matcher results: %i\n", secs * 1000.0, (int) mresvec.size());
if (mresvec.size()){
mresvec[0].transformation[2] = normalize_theta(mresvec[0].transformation[2]);
cerr << "Found Loop Closure Edge PI. Transf: " << mresvec[0].transformation.x() << " " << mresvec[0].transformation.y() << " " << mresvec[0].transformation.z() << endl;
CharGrid::addToPrunedMap(resultsMap, mresvec[0], dx, dy, dth);
}
for (std::map<DiscreteTriplet, MatcherResult>::iterator it = resultsMap.begin(); it!= resultsMap.end(); it++){
MatcherResult res = it->second;
Vector3d adj=res.transformation;
SE2 transf;
transf.setTranslation(Vector2d(adj.x(), adj.y()));
transf.setRotation(normalize_theta(adj.z()));
trel.push_back(transf);
std::cerr << "Final result: " << transf.translation().x() << " " << transf.translation().y() << " " << transf.rotation().angle() << std::endl;
}
if (trel.size())
return true;
return false;
}
void GraphSLAM::addDataSM(SE2 currentOdom, RobotLaser* laser){
boost::mutex::scoped_lock lockg(graphMutex);
//Add current vertex
VertexSE2 *v = new VertexSE2;
SE2 displacement = _lastOdom.inverse() * currentOdom;
SE2 currEst = _lastVertex->estimate() * displacement;
v->setEstimate(currEst);
v->setId(++_runningVertexId + idRobot() * baseId());
//Add covariance information
//VertexEllipse *ellipse = new VertexEllipse;
//Matrix3f cov = Matrix3f::Zero(); //last vertex has zero covariance
//ellipse->setCovariance(cov);
//v->setUserData(ellipse);
v->addUserData(laser);
std::cout << endl <<
"Current vertex: " << v->id() <<
" Estimate: "<< v->estimate().translation().x() <<
" " << v->estimate().translation().y() <<
" " << v->estimate().rotation().angle() << std::endl;
_graph->addVertex(v);
//Add current odometry edge
EdgeSE2 *e = new EdgeSE2;
e->setId(++_runningEdgeId + idRobot() * baseId());
e->vertices()[0] = _lastVertex;
e->vertices()[1] = v;
OptimizableGraph::VertexSet vset;
vset.insert(_lastVertex);
int j = 1;
int gap = 5;
while (j <= gap){
VertexSE2 *vj = dynamic_cast<VertexSE2 *>(graph()->vertex(_lastVertex->id()-j));
if (vj)
vset.insert(vj);
else
break;
j++;
}
SE2 transf;
bool shouldIAdd = _closeMatcher.closeScanMatching(vset, _lastVertex, v, &transf, maxScore);
if (shouldIAdd){
e->setMeasurement(transf);
e->setInformation(_SMinf);
}else{ //Trust the odometry
e->setMeasurement(displacement);
// Vector3d dis = displacement.toVector();
// dis.x() = fabs(dis.x());
// dis.y() = fabs(dis.y());
// dis.z() = fabs(dis.z());
// dis += Vector3d(1e-3,1e-3,1e-2);
// Matrix3d dis2 = dis*dis.transpose();
// Matrix3d newcov = dis2.cwiseProduct(_odomK);
// e->setInformation(newcov.inverse());
e->setInformation(_odominf);
}
_graph->addEdge(e);
_lastOdom = currentOdom;
_lastVertex = v;
}
bool ScanMatcher::verifyMatching(OptimizableGraph::VertexSet& vset1, OptimizableGraph::Vertex* _referenceVertex1,
OptimizableGraph::VertexSet& vset2, OptimizableGraph::Vertex* _referenceVertex2,
SE2 trel12, double *score){
VertexSE2* referenceVertex2=dynamic_cast<VertexSE2*>(_referenceVertex2);
resetGrid();
CharGrid auxGrid = _grid;
//Transform points from vset2 in the reference of referenceVertex1 using trel12
RawLaser::Point2DVector scansvset2inref1;
for (OptimizableGraph::VertexSet::iterator it = vset2.begin(); it != vset2.end(); it++){
VertexSE2 *vertex = (VertexSE2*) *it;
RobotLaser* laserv = dynamic_cast<RobotLaser*>(vertex->userData());
RawLaser::Point2DVector vscan = laserv->cartesian();
SE2 trl = laserv->laserParams().laserPose;
RawLaser::Point2DVector scanInRefVertex1;
if (vertex->id() == referenceVertex2->id()){
applyTransfToScan(trel12 * trl, vscan, scanInRefVertex1);
}else{
//Transform scans to the referenceVertex2 coordinates
SE2 tref2_v = referenceVertex2->estimate().inverse() * vertex->estimate();
applyTransfToScan(trel12 * tref2_v * trl, vscan, scanInRefVertex1);
}
scansvset2inref1.insert(scansvset2inref1.end(), scanInRefVertex1.begin(), scanInRefVertex1.end());
}
//Scans in vset1
RawLaser::Point2DVector scansvset1;
transformPointsFromVSet(vset1, _referenceVertex1, scansvset1);
//Add local map from vset2 into the grid
_grid.addAndConvolvePoints<RawLaser::Point2DVector>(scansvset2inref1.begin(), scansvset2inref1.end(), _kernel);
//Find points from vset1 not explained by map vset2
RawLaser::Point2DVector nonmatchedpoints;
_grid.searchNonMatchedPoints(scansvset1, nonmatchedpoints, .3);
//Add those points to a grid to count them
auxGrid.addAndConvolvePoints<RawLaser::Point2DVector>(nonmatchedpoints.begin(), nonmatchedpoints.end(), _kernel);
// ofstream image1;
// std::stringstream filename1;
// filename1 << "map2.ppm";
// image1.open(filename1.str().c_str());
// _LCGrid.grid().saveAsPPM(image1, false);
// ofstream image2;
// std::stringstream filename2;
// filename2 << "mapnonmatched.ppm";
// image2.open(filename2.str().c_str());
// auxGrid.grid().saveAsPPM(image2, false);
// //Just for saving the image
// resetLCGrid();
// _LCGrid.addAndConvolvePoints<RawLaser::Point2DVector>(scansvset1.begin(), scansvset1.end(), _LCKernel);
// ofstream image3;
// std::stringstream filename3;
// filename3 << "map1.ppm";
// image3.open(filename3.str().c_str());
// _LCGrid.grid().saveAsPPM(image3, false);
//Counting points around trel12
Vector2f lower(-.3+trel12.translation().x(), -.3+trel12.translation().y());
Vector2f upper(+.3+trel12.translation().x(), +.3+trel12.translation().y());
auxGrid.countPoints(lower, upper, score);
cerr << "Score: " << *score << endl;
double threshold = 40.0;
if (*score <= threshold)
return true;
return false;
}
