예제 #1
0
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;
}
  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;
  }
예제 #3
0
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;
}