bool SparseOptimizer::updateInitialization(HyperGraph::VertexSet& vset, HyperGraph::EdgeSet& eset)
  {
    std::vector<HyperGraph::Vertex*> newVertices;
    newVertices.reserve(vset.size());
    _activeVertices.reserve(_activeVertices.size() + vset.size());
    //for (HyperGraph::VertexSet::iterator it = vset.begin(); it != vset.end(); ++it)
      //_activeVertices.push_back(static_cast<OptimizableGraph::Vertex*>(*it));
    _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));

    // 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->setTempIndex(next);
          _ivMap.push_back(v);
          newVertices.push_back(v);
          _activeVertices.push_back(v);
          next++;
        }
        else // not supported right now
          abort();
      }
      else {
        v->setTempIndex(-1);
      }
    }

    //if (newVertices.size() != vset.size())
    //cerr << __PRETTY_FUNCTION__ << ": something went wrong " << PVAR(vset.size()) << " " << PVAR(newVertices.size()) << endl;
    return _solver->updateStructure(newVertices, eset);
  }
  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){
    if (edges().size() == 0) {
      cerr << __PRETTY_FUNCTION__ << ": Attempt to initialize an empty graph" << endl;
      return false;
    }
    bool workspaceAllocated = _jacobianWorkspace.allocate(); (void) workspaceAllocated;
    assert(workspaceAllocated && "Error while allocating memory for the Jacobians");
    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 && !e->allVerticesFixed()) {
            auxEdgeSet.insert(e);
            levelEdges++;
          }

        }
      }
      if (levelEdges){
        _activeVertices.push_back(v);

        // test for NANs in the current estimate if we are debugging
#      ifndef NDEBUG
        int estimateDim = v->estimateDimension();
        if (estimateDim > 0) {
          Eigen::VectorXd estimateData(estimateDim);
          if (v->getEstimateData(estimateData.data()) == true) {
            int k;
            bool hasNan = arrayHasNaN(estimateData.data(), estimateDim, &k);
            if (hasNan)
              cerr << __PRETTY_FUNCTION__ << ": Vertex " << v->id() << " contains a nan entry at index " << k << endl;
          }
        }
#      endif

      }
    }

    _activeEdges.reserve(auxEdgeSet.size());
    for (set<Edge*>::iterator it = auxEdgeSet.begin(); it != auxEdgeSet.end(); ++it)
      _activeEdges.push_back(*it);

    sortVectorContainers();
    return buildIndexMapping(_activeVertices);
  }
  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);
  }
示例#5
0
  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;
        }
      }
    }

    // solve orientation
    typedef LinearSolverCSparse<ScalarMatrix> SystemSolver;
    SystemSolver linearSystemSolver;
    linearSystemSolver.init();
    bool ok = linearSystemSolver.solve(H, x.data(), b.data());
    if (!ok) {
      cerr << __PRETTY_FUNCTION__ << "Failure while solving linear system" << endl;
      return false;
    }

    // update the orientation of the 2D poses and set translation to 0, GN shall solve that
    root->setToOrigin();
    for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {
      VertexSE2* v = static_cast<VertexSE2*>(_optimizer->indexMapping()[i]);
      int poseIdx = v->hessianIndex();
      SE2 poseUpdate(0, 0, normalize_theta(thetaGuess(poseIdx) + x(poseIdx)));
      v->setEstimate(poseUpdate);
    }

    return true;
  }