bool SparseOptimizer::gaugeFreedom()
{
if (vertices().empty())
return false;
int maxDim=0;
for (HyperGraph::VertexIDMap::iterator it=vertices().begin(); it!=vertices().end(); ++it){
OptimizableGraph::Vertex* v=static_cast<OptimizableGraph::Vertex*>(it->second);
maxDim = std::max(maxDim,v->dimension());
}
for (HyperGraph::VertexIDMap::iterator it=vertices().begin(); it!=vertices().end(); ++it){
OptimizableGraph::Vertex* v=static_cast<OptimizableGraph::Vertex*>(it->second);
if (v->dimension() == maxDim) {
// test for fixed vertex
if (v->fixed()) {
return false;
}
// test for full dimension prior
for (HyperGraph::EdgeSet::const_iterator eit = v->edges().begin(); eit != v->edges().end(); ++eit) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*eit);
if (e->vertices().size() == 1 && e->dimension() == maxDim)
return false;
}
}
}
return true;
}
bool OptimizableGraph::addEdge(HyperGraph::Edge* e_)
{
OptimizableGraph::Edge* e = dynamic_cast<OptimizableGraph::Edge*>(e_);
assert(e && "Edge does not inherit from OptimizableGraph::Edge");
// std::cerr << "subclass of OptimizableGraph::Edge confirmed";
if (! e)
return false;
bool eresult = HyperGraph::addEdge(e);
if (! eresult)
return false;
// std::cerr << "called HyperGraph::addEdge" << std::endl;
e->_internalId = _nextEdgeId++;
if (e->numUndefinedVertices())
return true;
// std::cerr << "internalId set" << std::endl;
if (! e->resolveParameters()){
cerr << __FUNCTION__ << ": FATAL, cannot resolve parameters for edge " << e << endl;
return false;
}
// std::cerr << "parameters set" << std::endl;
if (! e->resolveCaches()){
cerr << __FUNCTION__ << ": FATAL, cannot resolve caches for edge " << e << endl;
return false;
}
// std::cerr << "updating jacobian size" << std::endl;
_jacobianWorkspace.updateSize(e);
// std::cerr << "about to return true" << std::endl;
return true;
}
void MainWindow::setRobustKernel()
{
SparseOptimizer* optimizer = viewer->graph;
bool robustKernel = cbRobustKernel->isChecked();
double huberWidth = leKernelWidth->text().toDouble();
//odometry edges are those whose node ids differ by 1
bool onlyLoop = cbOnlyLoop->isChecked();
if (robustKernel) {
QString strRobustKernel = coRobustKernel->currentText();
AbstractRobustKernelCreator* creator = RobustKernelFactory::instance()->creator(strRobustKernel.toStdString());
if (! creator) {
cerr << strRobustKernel.toStdString() << " is not a valid robust kernel" << endl;
return;
}
for (SparseOptimizer::EdgeSet::const_iterator it = optimizer->edges().begin(); it != optimizer->edges().end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
if (onlyLoop) {
if (e->vertices().size() >= 2 && std::abs(e->vertex(0)->id() - e->vertex(1)->id()) != 1) {
e->setRobustKernel(creator->construct());
e->robustKernel()->setDelta(huberWidth);
}
} else {
e->setRobustKernel(creator->construct());
e->robustKernel()->setDelta(huberWidth);
}
}
} else {
for (SparseOptimizer::EdgeSet::const_iterator it = optimizer->edges().begin(); it != optimizer->edges().end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
e->setRobustKernel(0);
}
}
}
void MainWindow::setRobustKernel()
{
SparseOptimizer* optimizer = viewer->graph;
bool robustKernel = cbRobustKernel->isChecked();
double huberWidth = leKernelWidth->text().toDouble();
if (robustKernel) {
QString strRobustKernel = coRobustKernel->currentText();
AbstractRobustKernelCreator* creator = RobustKernelFactory::instance()->creator(strRobustKernel.toStdString());
if (! creator) {
cerr << strRobustKernel.toStdString() << " is not a valid robust kernel" << endl;
return;
}
for (SparseOptimizer::EdgeSet::const_iterator it = optimizer->edges().begin(); it != optimizer->edges().end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
e->setRobustKernel(creator->construct());
e->robustKernel()->setDelta(huberWidth);
}
} else {
for (SparseOptimizer::EdgeSet::const_iterator it = optimizer->edges().begin(); it != optimizer->edges().end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
e->setRobustKernel(0);
}
}
}
void SparseOptimizer::computeActiveErrors()
{
// call the callbacks in case there is something registered
HyperGraphActionSet& actions = _graphActions[AT_COMPUTEACTIVERROR];
if (actions.size() > 0) {
for (HyperGraphActionSet::iterator it = actions.begin(); it != actions.end(); ++it)
(*(*it))(this);
}
# ifdef G2O_OPENMP
# pragma omp parallel for default (shared) if (_activeEdges.size() > 50)
# endif
for (int k = 0; k < static_cast<int>(_activeEdges.size()); ++k) {
OptimizableGraph::Edge* e = _activeEdges[k];
e->computeError();
}
# ifndef NDEBUG
for (int k = 0; k < static_cast<int>(_activeEdges.size()); ++k) {
OptimizableGraph::Edge* e = _activeEdges[k];
bool hasNan = arrayHasNaN(e->errorData(), e->dimension());
if (hasNan) {
cerr << "computeActiveErrors(): found NaN in error for edge " << e << endl;
}
}
# endif
}
bool SparseOptimizer::updateInitialization(HyperGraph::VertexSet& vset, HyperGraph::EdgeSet& eset)
{
std::vector<HyperGraph::Vertex*> newVertices;
newVertices.reserve(vset.size());
_activeVertices.reserve(_activeVertices.size() + vset.size());
_activeEdges.reserve(_activeEdges.size() + eset.size());
for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
if (!e->allVerticesFixed()) _activeEdges.push_back(e);
}
// update the index mapping
size_t next = _ivMap.size();
for (HyperGraph::VertexSet::iterator it = vset.begin(); it != vset.end(); ++it) {
OptimizableGraph::Vertex* v=static_cast<OptimizableGraph::Vertex*>(*it);
if (! v->fixed()){
if (! v->marginalized()){
v->setHessianIndex(next);
_ivMap.push_back(v);
newVertices.push_back(v);
_activeVertices.push_back(v);
next++;
}
else // not supported right now
abort();
}
else {
v->setHessianIndex(-1);
}
}
//if (newVertices.size() != vset.size())
//cerr << __PRETTY_FUNCTION__ << ": something went wrong " << PVAR(vset.size()) << " " << PVAR(newVertices.size()) << endl;
return _algorithm->updateStructure(newVertices, eset);
}
bool BlockSolver<Traits>::buildSystem()
{
// clear b vector
# ifdef G2O_OPENMP
# pragma omp parallel for default (shared) if (_optimizer->indexMapping().size() > 1000)
# endif
for (int i = 0; i < static_cast<int>(_optimizer->indexMapping().size()); ++i) {
OptimizableGraph::Vertex* v=_optimizer->indexMapping()[i];
assert(v);
v->clearQuadraticForm();
}
_Hpp->clear();
if (_doSchur) {
_Hll->clear();
_Hpl->clear();
}
// resetting the terms for the pairwise constraints
// built up the current system by storing the Hessian blocks in the edges and vertices
# ifndef G2O_OPENMP
// no threading, we do not need to copy the workspace
JacobianWorkspace& jacobianWorkspace = _optimizer->jacobianWorkspace();
# else
// if running with threads need to produce copies of the workspace for each thread
JacobianWorkspace jacobianWorkspace = _optimizer->jacobianWorkspace();
# pragma omp parallel for default (shared) firstprivate(jacobianWorkspace) if (_optimizer->activeEdges().size() > 100)
# endif
for (int k = 0; k < static_cast<int>(_optimizer->activeEdges().size()); ++k) {
OptimizableGraph::Edge* e = _optimizer->activeEdges()[k];
e->linearizeOplus(jacobianWorkspace); // jacobian of the nodes' oplus (manifold)
e->constructQuadraticForm();
# ifndef NDEBUG
for (size_t i = 0; i < e->vertices().size(); ++i) {
const OptimizableGraph::Vertex* v = static_cast<const OptimizableGraph::Vertex*>(e->vertex(i));
if (! v->fixed()) {
bool hasANan = arrayHasNaN(jacobianWorkspace.workspaceForVertex(i), e->dimension() * v->dimension());
if (hasANan) {
std::cerr << "buildSystem(): NaN within Jacobian for edge " << e << " for vertex " << i << std::endl;
break;
}
}
}
# endif
}
// flush the current system in a sparse block matrix
# ifdef G2O_OPENMP
# pragma omp parallel for default (shared) if (_optimizer->indexMapping().size() > 1000)
# endif
for (int i = 0; i < static_cast<int>(_optimizer->indexMapping().size()); ++i) {
OptimizableGraph::Vertex* v=_optimizer->indexMapping()[i];
int iBase = v->colInHessian();
if (v->marginalized())
iBase+=_sizePoses;
v->copyB(_b+iBase);
}
return 0;
}
double EstimatePropagatorCost::operator()(OptimizableGraph::Edge* edge, const OptimizableGraph::VertexSet& from, OptimizableGraph::Vertex* to_) const
{
OptimizableGraph::Edge* e = dynamic_cast<OptimizableGraph::Edge*>(edge);
OptimizableGraph::Vertex* to = dynamic_cast<OptimizableGraph::Vertex*>(to_);
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 SparseOptimizer::computeActiveErrors()
{
for (EdgeContainer::const_iterator
it = _activeEdges.begin();
it != _activeEdges.end();
it++)
{
OptimizableGraph::Edge* e = *it;
e->computeError();
if (e->robustKernel()) e->robustifyError();
}
}
void SparseOptimizer::linearizeSystem()
{
# ifdef G2O_OPENMP
# pragma omp parallel for default (shared) if (_activeEdges.size() > 50)
# endif
for (size_t k = 0; k < _activeEdges.size(); ++k)
{
OptimizableGraph::Edge* e = _activeEdges[k];
// jacobian of the nodes' oplus (manifold)
e->linearizeOplus();
}
}
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);
}
double activeVertexChi(const OptimizableGraph::Vertex* v){
const SparseOptimizer* s = dynamic_cast<const SparseOptimizer*>(v->graph());
const OptimizableGraph::EdgeContainer& av = s->activeEdges();
double chi = 0;
int ne =0;
for (HyperGraph::EdgeSet::iterator it = v->edges().begin(); it!=v->edges().end(); it++){
OptimizableGraph::Edge* e = dynamic_cast <OptimizableGraph::Edge*> (*it);
if (!e)
continue;
if (s->findActiveEdge(e)!=av.end()) {
chi +=e->chi2();
ne++;
}
}
if (! ne)
return -1;
return chi/ne;
}
bool OptimizableGraph::addEdge(HyperGraph::Edge* e_)
{
OptimizableGraph::Edge* e = dynamic_cast<OptimizableGraph::Edge*>(e_);
assert(e && "Edge does not inherit from OptimizableGraph::Edge");
if (! e)
return false;
bool eresult = HyperGraph::addEdge(e);
if (! eresult)
return false;
e->_internalId = _nextEdgeId++;
if (! e->resolveParameters()){
cerr << __FUNCTION__ << ": FATAL, cannot resolve parameters for edge " << e << endl;
return false;
}
if (! e->resolveCaches()){
cerr << __FUNCTION__ << ": FATAL, cannot resolve caches for edge " << e << endl;
return false;
}
_jacobianWorkspace.updateSize(e);
return true;
}
bool OptimizableGraph::setEdgeVertex(HyperGraph::Edge* e, int pos, HyperGraph::Vertex* v){
if (! HyperGraph::setEdgeVertex(e,pos,v)){
return false;
}
if (!e->numUndefinedVertices()){
#ifndef NDEBUG
OptimizableGraph::Edge* ee = dynamic_cast<OptimizableGraph::Edge*>(e);
assert(ee && "Edge is not a OptimizableGraph::Edge");
#else
OptimizableGraph::Edge* ee = static_cast<OptimizableGraph::Edge*>(e);
#endif
if (! ee->resolveParameters()){
cerr << __FUNCTION__ << ": FATAL, cannot resolve parameters for edge " << e << endl;
return false;
}
if (! ee->resolveCaches()){
cerr << __FUNCTION__ << ": FATAL, cannot resolve caches for edge " << e << endl;
return false;
}
_jacobianWorkspace.updateSize(e);
}
return true;
}
bool BlockSolver<Traits>::updateStructure(const std::vector<HyperGraph::Vertex*>& vset, const HyperGraph::EdgeSet& edges)
{
for (std::vector<HyperGraph::Vertex*>::const_iterator vit = vset.begin(); vit != vset.end(); ++vit) {
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*vit);
int dim = v->dimension();
if (! v->marginalized()){
v->setColInHessian(_sizePoses);
_sizePoses+=dim;
_Hpp->rowBlockIndices().push_back(_sizePoses);
_Hpp->colBlockIndices().push_back(_sizePoses);
_Hpp->blockCols().push_back(typename SparseBlockMatrix<PoseMatrixType>::IntBlockMap());
++_numPoses;
int ind = v->hessianIndex();
PoseMatrixType* m = _Hpp->block(ind, ind, true);
v->mapHessianMemory(m->data());
} else {
std::cerr << "updateStructure(): Schur not supported" << std::endl;
abort();
}
}
resizeVector(_sizePoses + _sizeLandmarks);
for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
for (size_t viIdx = 0; viIdx < e->vertices().size(); ++viIdx) {
OptimizableGraph::Vertex* v1 = (OptimizableGraph::Vertex*) e->vertex(viIdx);
int ind1 = v1->hessianIndex();
int indexV1Bak = ind1;
if (ind1 == -1)
continue;
for (size_t vjIdx = viIdx + 1; vjIdx < e->vertices().size(); ++vjIdx) {
OptimizableGraph::Vertex* v2 = (OptimizableGraph::Vertex*) e->vertex(vjIdx);
int ind2 = v2->hessianIndex();
if (ind2 == -1)
continue;
ind1 = indexV1Bak;
bool transposedBlock = ind1 > ind2;
if (transposedBlock) // make sure, we allocate the upper triangular block
std::swap(ind1, ind2);
if (! v1->marginalized() && !v2->marginalized()) {
PoseMatrixType* m = _Hpp->block(ind1, ind2, true);
e->mapHessianMemory(m->data(), viIdx, vjIdx, transposedBlock);
} else {
std::cerr << __PRETTY_FUNCTION__ << ": not supported" << std::endl;
}
}
}
}
return true;
}
void SparseOptimizer::computeInitialGuess(EstimatePropagatorCost& costFunction)
{
OptimizableGraph::VertexSet emptySet;
std::set<Vertex*> backupVertices;
HyperGraph::VertexSet fixedVertices; // these are the root nodes where to start the initialization
for (EdgeContainer::iterator it = _activeEdges.begin(); it != _activeEdges.end(); ++it) {
OptimizableGraph::Edge* e = *it;
for (size_t i = 0; i < e->vertices().size(); ++i) {
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(e->vertex(i));
if (!v)
continue;
if (v->fixed())
fixedVertices.insert(v);
else { // check for having a prior which is able to fully initialize a vertex
for (EdgeSet::const_iterator vedgeIt = v->edges().begin(); vedgeIt != v->edges().end(); ++vedgeIt) {
OptimizableGraph::Edge* vedge = static_cast<OptimizableGraph::Edge*>(*vedgeIt);
if (vedge->vertices().size() == 1 && vedge->initialEstimatePossible(emptySet, v) > 0.) {
//cerr << "Initialize with prior for " << v->id() << endl;
vedge->initialEstimate(emptySet, v);
fixedVertices.insert(v);
}
}
}
if (v->hessianIndex() == -1) {
std::set<Vertex*>::const_iterator foundIt = backupVertices.find(v);
if (foundIt == backupVertices.end()) {
v->push();
backupVertices.insert(v);
}
}
}
}
EstimatePropagator estimatePropagator(this);
estimatePropagator.propagate(fixedVertices, costFunction);
// restoring the vertices that should not be initialized
for (std::set<Vertex*>::iterator it = backupVertices.begin(); it != backupVertices.end(); ++it) {
Vertex* v = *it;
v->pop();
}
if (verbose()) {
computeActiveErrors();
cerr << "iteration= -1\t chi2= " << activeChi2()
<< "\t time= 0.0"
<< "\t cumTime= 0.0"
<< "\t (using initial guess from " << costFunction.name() << ")" << endl;
}
}
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 BlockSolver<Traits>::buildStructure(bool zeroBlocks)
{
assert(_optimizer);
size_t sparseDim = 0;
_numPoses=0;
_numLandmarks=0;
_sizePoses=0;
_sizeLandmarks=0;
int* blockPoseIndices = new int[_optimizer->indexMapping().size()];
int* blockLandmarkIndices = new int[_optimizer->indexMapping().size()];
for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {
OptimizableGraph::Vertex* v = _optimizer->indexMapping()[i];
int dim = v->dimension();
if (! v->marginalized()){
v->setColInHessian(_sizePoses);
_sizePoses+=dim;
blockPoseIndices[_numPoses]=_sizePoses;
++_numPoses;
} else {
v->setColInHessian(_sizeLandmarks);
_sizeLandmarks+=dim;
blockLandmarkIndices[_numLandmarks]=_sizeLandmarks;
++_numLandmarks;
}
sparseDim += dim;
}
resize(blockPoseIndices, _numPoses, blockLandmarkIndices, _numLandmarks, sparseDim);
delete[] blockLandmarkIndices;
delete[] blockPoseIndices;
// allocate the diagonal on Hpp and Hll
int poseIdx = 0;
int landmarkIdx = 0;
for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {
OptimizableGraph::Vertex* v = _optimizer->indexMapping()[i];
if (! v->marginalized()){
//assert(poseIdx == v->hessianIndex());
PoseMatrixType* m = _Hpp->block(poseIdx, poseIdx, true);
if (zeroBlocks)
m->setZero();
v->mapHessianMemory(m->data());
++poseIdx;
} else {
LandmarkMatrixType* m = _Hll->block(landmarkIdx, landmarkIdx, true);
if (zeroBlocks)
m->setZero();
v->mapHessianMemory(m->data());
++landmarkIdx;
}
}
assert(poseIdx == _numPoses && landmarkIdx == _numLandmarks);
// temporary structures for building the pattern of the Schur complement
SparseBlockMatrixHashMap<PoseMatrixType>* schurMatrixLookup = 0;
if (_doSchur) {
schurMatrixLookup = new SparseBlockMatrixHashMap<PoseMatrixType>(_Hschur->rowBlockIndices(), _Hschur->colBlockIndices());
schurMatrixLookup->blockCols().resize(_Hschur->blockCols().size());
}
// here we assume that the landmark indices start after the pose ones
// create the structure in Hpp, Hll and in Hpl
for (SparseOptimizer::EdgeContainer::const_iterator it=_optimizer->activeEdges().begin(); it!=_optimizer->activeEdges().end(); ++it){
OptimizableGraph::Edge* e = *it;
for (size_t viIdx = 0; viIdx < e->vertices().size(); ++viIdx) {
OptimizableGraph::Vertex* v1 = (OptimizableGraph::Vertex*) e->vertex(viIdx);
int ind1 = v1->hessianIndex();
if (ind1 == -1)
continue;
int indexV1Bak = ind1;
for (size_t vjIdx = viIdx + 1; vjIdx < e->vertices().size(); ++vjIdx) {
OptimizableGraph::Vertex* v2 = (OptimizableGraph::Vertex*) e->vertex(vjIdx);
int ind2 = v2->hessianIndex();
if (ind2 == -1)
continue;
ind1 = indexV1Bak;
bool transposedBlock = ind1 > ind2;
if (transposedBlock){ // make sure, we allocate the upper triangle block
std::swap(ind1, ind2);
}
if (! v1->marginalized() && !v2->marginalized()){
PoseMatrixType* m = _Hpp->block(ind1, ind2, true);
if (zeroBlocks)
m->setZero();
e->mapHessianMemory(m->data(), viIdx, vjIdx, transposedBlock);
if (_Hschur) {// assume this is only needed in case we solve with the schur complement
schurMatrixLookup->addBlock(ind1, ind2);
}
} else if (v1->marginalized() && v2->marginalized()){
// RAINER hmm.... should we ever reach this here????
LandmarkMatrixType* m = _Hll->block(ind1-_numPoses, ind2-_numPoses, true);
if (zeroBlocks)
m->setZero();
e->mapHessianMemory(m->data(), viIdx, vjIdx, false);
} else {
if (v1->marginalized()){
PoseLandmarkMatrixType* m = _Hpl->block(v2->hessianIndex(),v1->hessianIndex()-_numPoses, true);
if (zeroBlocks)
m->setZero();
e->mapHessianMemory(m->data(), viIdx, vjIdx, true); // transpose the block before writing to it
} else {
PoseLandmarkMatrixType* m = _Hpl->block(v1->hessianIndex(),v2->hessianIndex()-_numPoses, true);
if (zeroBlocks)
m->setZero();
e->mapHessianMemory(m->data(), viIdx, vjIdx, false); // directly the block
}
}
}
}
}
if (! _doSchur)
return true;
_DInvSchur->diagonal().resize(landmarkIdx);
_Hpl->fillSparseBlockMatrixCCS(*_HplCCS);
for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {
OptimizableGraph::Vertex* v = _optimizer->indexMapping()[i];
if (v->marginalized()){
const HyperGraph::EdgeSet& vedges=v->edges();
for (HyperGraph::EdgeSet::const_iterator it1=vedges.begin(); it1!=vedges.end(); ++it1){
for (size_t i=0; i<(*it1)->vertices().size(); ++i)
{
OptimizableGraph::Vertex* v1= (OptimizableGraph::Vertex*) (*it1)->vertex(i);
if (v1->hessianIndex()==-1 || v1==v)
continue;
for (HyperGraph::EdgeSet::const_iterator it2=vedges.begin(); it2!=vedges.end(); ++it2){
for (size_t j=0; j<(*it2)->vertices().size(); ++j)
{
OptimizableGraph::Vertex* v2= (OptimizableGraph::Vertex*) (*it2)->vertex(j);
if (v2->hessianIndex()==-1 || v2==v)
continue;
int i1=v1->hessianIndex();
int i2=v2->hessianIndex();
if (i1<=i2) {
schurMatrixLookup->addBlock(i1, i2);
}
}
}
}
}
}
}
_Hschur->takePatternFromHash(*schurMatrixLookup);
delete schurMatrixLookup;
_Hschur->fillSparseBlockMatrixCCSTransposed(*_HschurTransposedCCS);
return true;
}
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;
}
bool SparseOptimizerIncremental::updateInitialization(HyperGraph::VertexSet& vset, HyperGraph::EdgeSet& eset)
{
if (batchStep) {
return SparseOptimizerOnline::updateInitialization(vset, eset);
}
for (HyperGraph::VertexSet::iterator it = vset.begin(); it != vset.end(); ++it) {
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
v->clearQuadraticForm(); // be sure that b is zero for this vertex
}
// get the touched vertices
_touchedVertices.clear();
for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
OptimizableGraph::Vertex* v1 = static_cast<OptimizableGraph::Vertex*>(e->vertices()[0]);
OptimizableGraph::Vertex* v2 = static_cast<OptimizableGraph::Vertex*>(e->vertices()[1]);
if (! v1->fixed())
_touchedVertices.insert(v1);
if (! v2->fixed())
_touchedVertices.insert(v2);
}
//cerr << PVAR(_touchedVertices.size()) << endl;
// updating the internal structures
std::vector<HyperGraph::Vertex*> newVertices;
newVertices.reserve(vset.size());
_activeVertices.reserve(_activeVertices.size() + vset.size());
_activeEdges.reserve(_activeEdges.size() + eset.size());
for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end(); ++it)
_activeEdges.push_back(static_cast<OptimizableGraph::Edge*>(*it));
//cerr << "updating internal done." << endl;
// update the index mapping
size_t next = _ivMap.size();
for (HyperGraph::VertexSet::iterator it = vset.begin(); it != vset.end(); ++it) {
OptimizableGraph::Vertex* v=static_cast<OptimizableGraph::Vertex*>(*it);
if (! v->fixed()){
if (! v->marginalized()){
v->setHessianIndex(next);
_ivMap.push_back(v);
newVertices.push_back(v);
_activeVertices.push_back(v);
next++;
}
else // not supported right now
abort();
}
else {
v->setHessianIndex(-1);
}
}
//cerr << "updating index mapping done." << endl;
// backup the tempindex and prepare sorting structure
VertexBackup backupIdx[_touchedVertices.size()];
memset(backupIdx, 0, sizeof(VertexBackup) * _touchedVertices.size());
int idx = 0;
for (HyperGraph::VertexSet::iterator it = _touchedVertices.begin(); it != _touchedVertices.end(); ++it) {
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
backupIdx[idx].hessianIndex = v->hessianIndex();
backupIdx[idx].vertex = v;
backupIdx[idx].hessianData = v->hessianData();
++idx;
}
sort(backupIdx, backupIdx + _touchedVertices.size()); // sort according to the hessianIndex which is the same order as used later by the optimizer
for (int i = 0; i < idx; ++i) {
backupIdx[i].vertex->setHessianIndex(i);
}
//cerr << "backup tempindex done." << endl;
// building the structure of the update
_updateMat.clear(true); // get rid of the old matrix structure
_updateMat.rowBlockIndices().clear();
_updateMat.colBlockIndices().clear();
_updateMat.blockCols().clear();
// placing the current stuff in _updateMat
MatrixXd* lastBlock = 0;
int sizePoses = 0;
for (int i = 0; i < idx; ++i) {
OptimizableGraph::Vertex* v = backupIdx[i].vertex;
int dim = v->dimension();
sizePoses+=dim;
_updateMat.rowBlockIndices().push_back(sizePoses);
_updateMat.colBlockIndices().push_back(sizePoses);
_updateMat.blockCols().push_back(SparseBlockMatrix<MatrixXd>::IntBlockMap());
int ind = v->hessianIndex();
//cerr << PVAR(ind) << endl;
if (ind >= 0) {
MatrixXd* m = _updateMat.block(ind, ind, true);
v->mapHessianMemory(m->data());
lastBlock = m;
}
}
lastBlock->diagonal().array() += 1e-6; // HACK to get Eigen value > 0
for (HyperGraph::EdgeSet::const_iterator it = eset.begin(); it != eset.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
OptimizableGraph::Vertex* v1 = (OptimizableGraph::Vertex*) e->vertices()[0];
OptimizableGraph::Vertex* v2 = (OptimizableGraph::Vertex*) e->vertices()[1];
int ind1 = v1->hessianIndex();
if (ind1 == -1)
continue;
int ind2 = v2->hessianIndex();
if (ind2 == -1)
continue;
bool transposedBlock = ind1 > ind2;
if (transposedBlock) // make sure, we allocate the upper triangular block
swap(ind1, ind2);
MatrixXd* m = _updateMat.block(ind1, ind2, true);
e->mapHessianMemory(m->data(), 0, 1, transposedBlock);
}
// build the system into _updateMat
for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end(); ++it) {
OptimizableGraph::Edge * e = static_cast<OptimizableGraph::Edge*>(*it);
e->computeError();
}
for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
e->linearizeOplus();
}
for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
e->constructQuadraticForm();
}
// restore the original data for the vertex
for (int i = 0; i < idx; ++i) {
backupIdx[i].vertex->setHessianIndex(backupIdx[i].hessianIndex);
if (backupIdx[i].hessianData)
backupIdx[i].vertex->mapHessianMemory(backupIdx[i].hessianData);
}
// update the structure of the real block matrix
bool solverStatus = _algorithm->updateStructure(newVertices, eset);
bool updateStatus = computeCholeskyUpdate();
if (! updateStatus) {
cerr << "Error while computing update" << endl;
}
cholmod_sparse* updateAsSparseFactor = cholmod_factor_to_sparse(_cholmodFactor, &_cholmodCommon);
// convert CCS update by permuting back to the permutation of L
if (updateAsSparseFactor->nzmax > _permutedUpdate->nzmax) {
//cerr << "realloc _permutedUpdate" << endl;
cholmod_reallocate_triplet(updateAsSparseFactor->nzmax, _permutedUpdate, &_cholmodCommon);
}
_permutedUpdate->nnz = 0;
_permutedUpdate->nrow = _permutedUpdate->ncol = _L->n;
{
int* Ap = (int*)updateAsSparseFactor->p;
int* Ai = (int*)updateAsSparseFactor->i;
double* Ax = (double*)updateAsSparseFactor->x;
int* Bj = (int*)_permutedUpdate->j;
int* Bi = (int*)_permutedUpdate->i;
double* Bx = (double*)_permutedUpdate->x;
for (size_t c = 0; c < updateAsSparseFactor->ncol; ++c) {
const int& rbeg = Ap[c];
const int& rend = Ap[c+1];
int cc = c / slamDimension;
int coff = c % slamDimension;
const int& cbase = backupIdx[cc].vertex->colInHessian();
const int& ccol = _perm(cbase + coff);
for (int j = rbeg; j < rend; j++) {
const int& r = Ai[j];
const double& val = Ax[j];
int rr = r / slamDimension;
int roff = r % slamDimension;
const int& rbase = backupIdx[rr].vertex->colInHessian();
int row = _perm(rbase + roff);
int col = ccol;
if (col > row) // lower triangular entry
swap(col, row);
Bi[_permutedUpdate->nnz] = row;
Bj[_permutedUpdate->nnz] = col;
Bx[_permutedUpdate->nnz] = val;
++_permutedUpdate->nnz;
}
}
}
cholmod_free_sparse(&updateAsSparseFactor, &_cholmodCommon);
#if 0
cholmod_sparse* updatePermuted = cholmod_triplet_to_sparse(_permutedUpdate, _permutedUpdate->nnz, &_cholmodCommon);
//writeCCSMatrix("update-permuted.txt", updatePermuted->nrow, updatePermuted->ncol, (int*)updatePermuted->p, (int*)updatePermuted->i, (double*)updatePermuted->x, false);
_solverInterface->choleskyUpdate(updatePermuted);
cholmod_free_sparse(&updatePermuted, &_cholmodCommon);
#else
convertTripletUpdateToSparse();
_solverInterface->choleskyUpdate(_permutedUpdateAsSparse);
#endif
return solverStatus;
}
bool SparseOptimizer::initializeOptimization
(HyperGraph::VertexSet& vset, int level)
{
// Recorre todos los vertices introducidos en el optimizador.
// Para cada vertice 'V' obtiene los edges de los que forma parte.
// Para cada uno de esos edges, se mira si todos sus vertices estan en el
// optimizador. Si lo estan, el edge se aniade a _activeEdges.
// Si el vertice 'V' tiene algun edge con todos los demas vertices en el
// optimizador, se aniade 'V' a _activeVertices
// Al final se asignan unos indices internos para los vertices:
// -1: vertices fijos
// 0..n: vertices no fijos y NO marginalizables
// n+1..m: vertices no fijos y marginalizables
clearIndexMapping();
_activeVertices.clear();
_activeVertices.reserve(vset.size());
_activeEdges.clear();
set<Edge*> auxEdgeSet; // temporary structure to avoid duplicates
for (HyperGraph::VertexSet::iterator
it = vset.begin();
it != vset.end();
it++)
{
OptimizableGraph::Vertex* v= (OptimizableGraph::Vertex*) *it;
const OptimizableGraph::EdgeSet& vEdges=v->edges();
// count if there are edges in that level. If not remove from the pool
int levelEdges=0;
for (OptimizableGraph::EdgeSet::const_iterator
it = vEdges.begin();
it != vEdges.end();
it++)
{
OptimizableGraph::Edge* e =
reinterpret_cast<OptimizableGraph::Edge*>(*it);
if (level < 0 || e->level() == level)
{
bool allVerticesOK = true;
for (vector<HyperGraph::Vertex*>::const_iterator
vit = e->vertices().begin();
vit != e->vertices().end();
++vit)
{
if (vset.find(*vit) == vset.end())
{
allVerticesOK = false;
break;
}
}
if (allVerticesOK)
{
auxEdgeSet.insert(reinterpret_cast<OptimizableGraph::Edge*>(*it));
levelEdges++;
}
}
}
if (levelEdges) _activeVertices.push_back(v);
}
_activeEdges.reserve(auxEdgeSet.size());
for (set<Edge*>::iterator
it = auxEdgeSet.begin();
it != auxEdgeSet.end();
++it)
_activeEdges.push_back(*it);
sortVectorContainers();
return buildIndexMapping(_activeVertices);
}
int SparseOptimizerIncremental::optimize(int iterations, bool online)
{
(void) iterations; // we only do one iteration anyhow
OptimizationAlgorithm* solver = _algorithm;
solver->init(online);
int cjIterations=0;
bool ok=true;
if (! online || batchStep) {
//cerr << "performing batch step" << endl;
if (! online) {
ok = _underlyingSolver->buildStructure();
if (! ok) {
cerr << __PRETTY_FUNCTION__ << ": Failure while building CCS structure" << endl;
return 0;
}
}
// copy over the updated estimate as new linearization point
if (slamDimension == 3) {
for (size_t i = 0; i < indexMapping().size(); ++i) {
OnlineVertexSE2* v = static_cast<OnlineVertexSE2*>(indexMapping()[i]);
v->setEstimate(v->updatedEstimate);
}
}
else if (slamDimension == 6) {
for (size_t i = 0; i < indexMapping().size(); ++i) {
OnlineVertexSE3* v= static_cast<OnlineVertexSE3*>(indexMapping()[i]);
v->setEstimate(v->updatedEstimate);
}
}
SparseOptimizer::computeActiveErrors();
SparseOptimizer::linearizeSystem();
_underlyingSolver->buildSystem();
int numBlocksRequired = _ivMap.size();
if (_cmember.size() < numBlocksRequired) {
_cmember.resize(2 * numBlocksRequired);
}
memset(_cmember.data(), 0, numBlocksRequired * sizeof(int));
if (_ivMap.size() > 100) {
for (size_t i = _ivMap.size() - 20; i < _ivMap.size(); ++i) {
const HyperGraph::EdgeSet& eset = _ivMap[i]->edges();
for (HyperGraph::EdgeSet::const_iterator it = eset.begin(); it != eset.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
OptimizableGraph::Vertex* v1 = static_cast<OptimizableGraph::Vertex*>(e->vertices()[0]);
OptimizableGraph::Vertex* v2 = static_cast<OptimizableGraph::Vertex*>(e->vertices()[1]);
if (v1->hessianIndex() >= 0)
_cmember(v1->hessianIndex()) = 1;
if (v2->hessianIndex() >= 0)
_cmember(v2->hessianIndex()) = 1;
}
}
}
ok = _underlyingSolver->solve();
// get the current cholesky factor along with the permutation
_L = _solverInterface->L();
if (_perm.size() < (int)_L->n)
_perm.resize(2*_L->n);
int* p = (int*)_L->Perm;
for (size_t i = 0; i < _L->n; ++i)
_perm[p[i]] = i;
}
else {
// update the b vector
for (HyperGraph::VertexSet::iterator it = _touchedVertices.begin(); it != _touchedVertices.end(); ++it) {
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
int iBase = v->colInHessian();
v->copyB(_underlyingSolver->b() + iBase);
}
_solverInterface->solve(_underlyingSolver->x(), _underlyingSolver->b());
}
update(_underlyingSolver->x());
++cjIterations;
if (verbose()){
computeActiveErrors();
cerr
<< "nodes = " << vertices().size()
<< "\t edges= " << _activeEdges.size()
<< "\t chi2= " << FIXED(activeChi2())
<< endl;
}
if (vizWithGnuplot)
gnuplotVisualization();
if (! ok)
return 0;
return 1;
}
bool saveGnuplot(const std::string& gnudump, const HyperGraph::VertexSet& vertices, const HyperGraph::EdgeSet& edges)
{
// seek for an action whose name is writeGnuplot in the library
HyperGraphElementAction* saveGnuplot = HyperGraphActionLibrary::instance()->actionByName("writeGnuplot");
if (! saveGnuplot ){
cerr << __PRETTY_FUNCTION__ << ": no action \"writeGnuplot\" registered" << endl;
return false;
}
WriteGnuplotAction::Parameters params;
int maxDim = -1;
int minDim = numeric_limits<int>::max();
for (HyperGraph::VertexSet::const_iterator it = vertices.begin(); it != vertices.end(); ++it){
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
int vdim = v->dimension();
maxDim = (std::max)(vdim, maxDim);
minDim = (std::min)(vdim, minDim);
}
string extension = getFileExtension(gnudump);
if (extension.size() == 0)
extension = "dat";
string baseFilename = getPureFilename(gnudump);
// check for odometry edges
bool hasOdomEdge = false;
bool hasLandmarkEdge = false;
for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
if (e->vertices().size() == 2) {
if (edgeAllVertsSameDim(e, maxDim))
hasOdomEdge = true;
else
hasLandmarkEdge = true;
}
if (hasOdomEdge && hasLandmarkEdge)
break;
}
bool fileStatus = true;
if (hasOdomEdge) {
string odomFilename = baseFilename + "_odom_edges." + extension;
cerr << "# saving " << odomFilename << " ... ";
ofstream fout(odomFilename.c_str());
if (! fout) {
cerr << "Unable to open file" << endl;
return false;
}
params.os = &fout;
// writing odometry edges
for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
if (e->vertices().size() != 2 || ! edgeAllVertsSameDim(e, maxDim))
continue;
(*saveGnuplot)(e, ¶ms);
}
cerr << "done." << endl;
}
if (hasLandmarkEdge) {
string filename = baseFilename + "_landmarks_edges." + extension;
cerr << "# saving " << filename << " ... ";
ofstream fout(filename.c_str());
if (! fout) {
cerr << "Unable to open file" << endl;
return false;
}
params.os = &fout;
// writing landmark edges
for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
if (e->vertices().size() != 2 || edgeAllVertsSameDim(e, maxDim))
continue;
(*saveGnuplot)(e, ¶ms);
}
cerr << "done." << endl;
}
if (1) {
string filename = baseFilename + "_edges." + extension;
cerr << "# saving " << filename << " ... ";
ofstream fout(filename.c_str());
if (! fout) {
cerr << "Unable to open file" << endl;
return false;
}
params.os = &fout;
// writing all edges
for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
(*saveGnuplot)(e, ¶ms);
}
cerr << "done." << endl;
}
if (1) {
string filename = baseFilename + "_vertices." + extension;
cerr << "# saving " << filename << " ... ";
ofstream fout(filename.c_str());
if (! fout) {
cerr << "Unable to open file" << endl;
return false;
}
params.os = &fout;
for (HyperGraph::VertexSet::const_iterator it = vertices.begin(); it != vertices.end(); ++it){
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
(*saveGnuplot)(v, ¶ms);
}
cerr << "done." << endl;
}
return fileStatus;
}
