C++ Vertex::hessianIndex方法代码示例

 bool SparseOptimizer::removeVertex(HyperGraph::Vertex* v)
 {
   OptimizableGraph::Vertex* vv = static_cast<OptimizableGraph::Vertex*>(v);
   if (vv->hessianIndex() >= 0) {
     clearIndexMapping();
     _ivMap.clear();
   }
   return HyperGraph::removeVertex(v);
 }

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;
    }
  }

  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);
//.........这里部分代码省略.........

  bool Star::labelStarEdges(int iterations, EdgeLabeler* labeler){
    // mark all vertices in the lowLevelEdges as floating
    bool ok=true;
    std::set<OptimizableGraph::Vertex*> vset;
    for (HyperGraph::EdgeSet::iterator it=_lowLevelEdges.begin(); it!=_lowLevelEdges.end(); it++){
      HyperGraph::Edge* e=*it;
      for (size_t i=0; i<e->vertices().size(); i++){
        OptimizableGraph::Vertex* v=(OptimizableGraph::Vertex*)e->vertices()[i];
        v->setFixed(false);
        vset.insert(v);
      }
    }
    for (std::set<OptimizableGraph::Vertex*>::iterator it=vset.begin(); it!=vset.end(); it++){
      OptimizableGraph::Vertex* v = *it;
      v->push();
    }

    // fix all vertices in the gauge
    //cerr << "fixing gauge: ";
    for (HyperGraph::VertexSet::iterator it = _gauge.begin(); it!=_gauge.end(); it++){
      OptimizableGraph::Vertex* v=(OptimizableGraph::Vertex*)*it;
      //cerr << v->id() << " ";
      v->setFixed(true);
    }
    //cerr << endl;
    if (iterations>0){
      _optimizer->initializeOptimization(_lowLevelEdges);
      _optimizer->computeInitialGuess();
      int result=_optimizer->optimize(iterations);
      if (result<1){
        cerr << "Vertices num: " << _optimizer->activeVertices().size() << "ids: ";
        for (size_t i=0; i<_optimizer->indexMapping().size(); i++){
          cerr << _optimizer->indexMapping()[i]->id() << " " ;
        }
        cerr << endl;
        cerr << "!!! optimization failure" << endl;
        cerr << "star size=" << _lowLevelEdges.size() << endl;
        cerr << "gauge: ";
        for (HyperGraph::VertexSet::iterator it=_gauge.begin(); it!=_gauge.end(); it++){
          OptimizableGraph::Vertex* v = (OptimizableGraph::Vertex*)*it;
          cerr << "[" << v->id() << " " << v->hessianIndex() << "] ";
        }
        cerr << endl;
        ok=false;
      }
    }  else {
      optimizer()->initializeOptimization(_lowLevelEdges);
      // cerr << "guess" << endl;
      //optimizer()->computeInitialGuess();
      // cerr << "solver init" << endl;
      optimizer()->solver()->init();
      // cerr << "structure" << endl;
      OptimizationAlgorithmWithHessian* solverWithHessian = dynamic_cast<OptimizationAlgorithmWithHessian*> (optimizer()->solver());
      if (!solverWithHessian->buildLinearStructure())
        cerr << "FATAL: failure while building linear structure" << endl;
      // cerr << "errors" << endl;
      optimizer()->computeActiveErrors();
      // cerr << "system" << endl;
      solverWithHessian->updateLinearSystem();
    }

    std::set<OptimizableGraph::Edge*> star;
    for(HyperGraph::EdgeSet::iterator it=_starEdges.begin(); it!=_starEdges.end(); it++){
      star.insert((OptimizableGraph::Edge*)*it);
    }
    if (ok) {
      int result = labeler->labelEdges(star);
      if (result < 0)
        ok=false;
    }
    // release all vertices in the gauge
    for (std::set<OptimizableGraph::Vertex*>::iterator it=vset.begin(); it!=vset.end(); it++){
      OptimizableGraph::Vertex* v = *it;
      v->pop();
    }
    for (HyperGraph::VertexSet::iterator it=_gauge.begin(); it!=_gauge.end(); it++){
      OptimizableGraph::Vertex* v=(OptimizableGraph::Vertex*)*it;
      v->setFixed(false);
    }

    return ok;
  }

  bool SolverSLAM2DLinear::solveOrientation()
  {
    assert(_optimizer->indexMapping().size() + 1 == _optimizer->vertices().size() && "Needs to operate on full graph");
    assert(_optimizer->vertex(0)->fixed() && "Graph is not fixed by vertex 0");
    VectorXD b, x; // will be used for theta and x/y update
    b.setZero(_optimizer->indexMapping().size());
    x.setZero(_optimizer->indexMapping().size());

    typedef Eigen::Matrix<double, 1, 1, Eigen::ColMajor> ScalarMatrix;

    ScopedArray<int> blockIndeces(new int[_optimizer->indexMapping().size()]);
    for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i)
      blockIndeces[i] = i+1;

    SparseBlockMatrix<ScalarMatrix> H(blockIndeces.get(), blockIndeces.get(), _optimizer->indexMapping().size(), _optimizer->indexMapping().size());

    // building the structure, diagonal for each active vertex
    for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {
      OptimizableGraph::Vertex* v = _optimizer->indexMapping()[i];
      int poseIdx = v->hessianIndex();
      ScalarMatrix* m = H.block(poseIdx, poseIdx, true);
      m->setZero();
    }

    HyperGraph::VertexSet fixedSet;

    // off diagonal for each edge
    for (SparseOptimizer::EdgeContainer::const_iterator it = _optimizer->activeEdges().begin(); it != _optimizer->activeEdges().end(); ++it) {
#    ifndef NDEBUG
      EdgeSE2* e = dynamic_cast<EdgeSE2*>(*it);
      assert(e && "Active edges contain non-odometry edge"); //
#    else
      EdgeSE2* e = static_cast<EdgeSE2*>(*it);
#    endif
      OptimizableGraph::Vertex* from = static_cast<OptimizableGraph::Vertex*>(e->vertices()[0]);
      OptimizableGraph::Vertex* to   = static_cast<OptimizableGraph::Vertex*>(e->vertices()[1]);

      int ind1 = from->hessianIndex();
      int ind2 = to->hessianIndex();
      if (ind1 == -1 || ind2 == -1) {
        if (ind1 == -1) fixedSet.insert(from); // collect the fixed vertices
        if (ind2 == -1) fixedSet.insert(to);
        continue;
      }

      bool transposedBlock = ind1 > ind2;
      if (transposedBlock){ // make sure, we allocate the upper triangle block
        std::swap(ind1, ind2);
      }

      ScalarMatrix* m = H.block(ind1, ind2, true);
      m->setZero();
    }

    // walk along the Minimal Spanning Tree to compute the guess for the robot orientation
    assert(fixedSet.size() == 1);
    VertexSE2* root = static_cast<VertexSE2*>(*fixedSet.begin());
    VectorXD thetaGuess;
    thetaGuess.setZero(_optimizer->indexMapping().size());
    UniformCostFunction uniformCost;
    HyperDijkstra hyperDijkstra(_optimizer);
    hyperDijkstra.shortestPaths(root, &uniformCost);

    HyperDijkstra::computeTree(hyperDijkstra.adjacencyMap());
    ThetaTreeAction thetaTreeAction(thetaGuess.data());
    HyperDijkstra::visitAdjacencyMap(hyperDijkstra.adjacencyMap(), &thetaTreeAction);

    // construct for the orientation
    for (SparseOptimizer::EdgeContainer::const_iterator it = _optimizer->activeEdges().begin(); it != _optimizer->activeEdges().end(); ++it) {
      EdgeSE2* e = static_cast<EdgeSE2*>(*it);
      VertexSE2* from = static_cast<VertexSE2*>(e->vertices()[0]);
      VertexSE2* to   = static_cast<VertexSE2*>(e->vertices()[1]);

      double omega = e->information()(2,2);

      double fromThetaGuess = from->hessianIndex() < 0 ? 0. : thetaGuess[from->hessianIndex()];
      double toThetaGuess   = to->hessianIndex() < 0 ? 0. : thetaGuess[to->hessianIndex()];
      double error          = normalize_theta(-e->measurement().rotation().angle() + toThetaGuess - fromThetaGuess);

      bool fromNotFixed = !(from->fixed());
      bool toNotFixed   = !(to->fixed());

      if (fromNotFixed || toNotFixed) {
        double omega_r = - omega * error;
        if (fromNotFixed) {
          b(from->hessianIndex()) -= omega_r;
          (*H.block(from->hessianIndex(), from->hessianIndex()))(0,0) += omega;
          if (toNotFixed) {
            if (from->hessianIndex() > to->hessianIndex())
              (*H.block(to->hessianIndex(), from->hessianIndex()))(0,0) -= omega;
            else
              (*H.block(from->hessianIndex(), to->hessianIndex()))(0,0) -= omega;
          }
        } 
        if (toNotFixed ) {
          b(to->hessianIndex()) += omega_r;
          (*H.block(to->hessianIndex(), to->hessianIndex()))(0,0) += omega;
        }
      }
    }
//.........这里部分代码省略.........