sparse_optimizer.cpp

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
//   this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
//   notice, this list of conditions and the following disclaimer in the
//   documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
#include "sparse_optimizer.h"
 
#include <algorithm>
#include <cassert>
#include <iomanip>
#include <iostream>
#include <iterator>
#include <utility>
 
#include "batch_stats.h"
#include "estimate_propagator.h"
#include "g2o/config.h"
#include "g2o/core/optimizable_graph.h"
#include "g2o/core/ownership.h"
#include "g2o/stuff/logger.h"
#include "g2o/stuff/macros.h"
#include "g2o/stuff/misc.h"
#include "g2o/stuff/timeutil.h"
#include "hyper_graph_action.h"
#include "optimization_algorithm.h"
#include "robust_kernel.h"
 
namespace g2o {
using namespace std;
 
SparseOptimizer::SparseOptimizer()
    : _forceStopFlag(0),
      _verbose(false),
      _algorithm(nullptr),
      _computeBatchStatistics(false) {
  _graphActions.resize(AT_NUM_ELEMENTS);
}
 
SparseOptimizer::~SparseOptimizer() {
  release(_algorithm);
  G2OBatchStatistics::setGlobalStats(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) {
      G2O_WARN("computeActiveErrors(): found NaN in error for edge {}",
               static_cast<void*>(e));
    }
  }
#endif
}
 
double SparseOptimizer::activeChi2() const {
  double chi = 0.0;
  for (EdgeContainer::const_iterator it = _activeEdges.begin();
       it != _activeEdges.end(); ++it) {
    const OptimizableGraph::Edge* e = *it;
    chi += e->chi2();
  }
  return chi;
}
 
double SparseOptimizer::activeRobustChi2() const {
  Vector3 rho;
  double chi = 0.0;
  for (EdgeContainer::const_iterator it = _activeEdges.begin();
       it != _activeEdges.end(); ++it) {
    const OptimizableGraph::Edge* e = *it;
    if (e->robustKernel()) {
      e->robustKernel()->robustify(e->chi2(), rho);
      chi += rho[0];
    } else
      chi += e->chi2();
  }
  return chi;
}
 
OptimizableGraph::Vertex* SparseOptimizer::findGauge() {
  if (vertices().empty()) return nullptr;
 
  int maxDim = maxDimension();
 
  OptimizableGraph::Vertex* rut = 0;
  for (HyperGraph::VertexIDMap::iterator it = vertices().begin();
       it != vertices().end(); ++it) {
    OptimizableGraph::Vertex* v =
        static_cast<OptimizableGraph::Vertex*>(it->second);
    if (v->dimension() == maxDim) {
      rut = v;
      break;
    }
  }
  return rut;
}
 
bool SparseOptimizer::gaugeFreedom() {
  if (vertices().empty()) return false;
 
  int maxDim = maxDimension();
 
  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 SparseOptimizer::buildIndexMapping(
    SparseOptimizer::VertexContainer& vlist) {
  if (!vlist.size()) {
    _ivMap.clear();
    return false;
  }
 
  _ivMap.resize(vlist.size());
  size_t i = 0;
  for (int k = 0; k < 2; k++)
    for (VertexContainer::iterator it = vlist.begin(); it != vlist.end();
         ++it) {
      OptimizableGraph::Vertex* v = *it;
      if (!v->fixed()) {
        if (static_cast<int>(v->marginalized()) == k) {
          v->setHessianIndex(i);
          _ivMap[i] = v;
          i++;
        }
      } else {
        v->setHessianIndex(-1);
      }
    }
  _ivMap.resize(i);
  return true;
}
 
void SparseOptimizer::clearIndexMapping() {
  for (size_t i = 0; i < _ivMap.size(); ++i) {
    _ivMap[i]->setHessianIndex(-1);
    _ivMap[i] = 0;
  }
}
 
bool SparseOptimizer::initializeOptimization(int level) {
  HyperGraph::VertexSet vset;
  for (VertexIDMap::iterator it = vertices().begin(); it != vertices().end();
       ++it)
    vset.insert(it->second);
  return initializeOptimization(vset, level);
}
 
bool SparseOptimizer::initializeOptimization(HyperGraph::VertexSet& vset,
                                             int level) {
  if (edges().size() == 0) {
    G2O_WARN("Attempt to initialize an empty graph");
    return false;
  }
  preIteration(-1);
  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) {
        VectorX estimateData(estimateDim);
        if (v->getEstimateData(estimateData.data()) == true) {
          int k;
          bool hasNan = arrayHasNaN(estimateData.data(), estimateDim, &k);
          if (hasNan)
            G2O_WARN("{}: Vertex {} contains a nan entry at index {}",
                     __PRETTY_FUNCTION__, v->id(), k);
        }
      }
#endif
    }
  }
 
  _activeEdges.reserve(auxEdgeSet.size());
  for (set<Edge*>::iterator it = auxEdgeSet.begin(); it != auxEdgeSet.end();
       ++it)
    _activeEdges.push_back(*it);
 
  sortVectorContainers();
  bool indexMappingStatus = buildIndexMapping(_activeVertices);
  postIteration(-1);
  return indexMappingStatus;
}
 
bool SparseOptimizer::initializeOptimization(HyperGraph::EdgeSet& eset) {
  preIteration(-1);
  bool workspaceAllocated = _jacobianWorkspace.allocate();
  (void)workspaceAllocated;
  assert(workspaceAllocated &&
         "Error while allocating memory for the Jacobians");
  clearIndexMapping();
  _activeVertices.clear();
  _activeEdges.clear();
  _activeEdges.reserve(eset.size());
  set<Vertex*> auxVertexSet;  // temporary structure to avoid duplicates
  for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end();
       ++it) {
    OptimizableGraph::Edge* e = (OptimizableGraph::Edge*)(*it);
    if (e->numUndefinedVertices()) continue;
    for (vector<HyperGraph::Vertex*>::const_iterator vit =
             e->vertices().begin();
         vit != e->vertices().end(); ++vit) {
      auxVertexSet.insert(static_cast<OptimizableGraph::Vertex*>(*vit));
    }
    _activeEdges.push_back(reinterpret_cast<OptimizableGraph::Edge*>(*it));
  }
 
  _activeVertices.reserve(auxVertexSet.size());
  for (set<Vertex*>::iterator it = auxVertexSet.begin();
       it != auxVertexSet.end(); ++it)
    _activeVertices.push_back(*it);
 
  sortVectorContainers();
  bool indexMappingStatus = buildIndexMapping(_activeVertices);
  postIteration(-1);
  return indexMappingStatus;
}
 
void SparseOptimizer::setToOrigin() {
  for (VertexIDMap::iterator it = vertices().begin(); it != vertices().end();
       ++it) {
    OptimizableGraph::Vertex* v =
        static_cast<OptimizableGraph::Vertex*>(it->second);
    v->setToOrigin();
  }
}
 
void SparseOptimizer::computeInitialGuess() {
  EstimatePropagator::PropagateCost costFunction(this);
  computeInitialGuess(costFunction);
}
 
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.) {
            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;
  }
}
 
int SparseOptimizer::optimize(int iterations, bool online) {
  if (_ivMap.size() == 0) {
    G2O_WARN(
        "{}: 0 vertices to optimize, maybe forgot to call "
        "initializeOptimization()",
        __PRETTY_FUNCTION__);
    return -1;
  }
 
  int cjIterations = 0;
  double cumTime = 0;
  bool ok = true;
 
  ok = _algorithm->init(online);
  if (!ok) {
    G2O_ERROR("{}: Error while initializing", __PRETTY_FUNCTION__);
    return -1;
  }
 
  _batchStatistics.clear();
  if (_computeBatchStatistics) _batchStatistics.resize(iterations);
 
  OptimizationAlgorithm::SolverResult result = OptimizationAlgorithm::OK;
  for (int i = 0; i < iterations && !terminate() && ok; i++) {
    preIteration(i);
 
    if (_computeBatchStatistics) {
      G2OBatchStatistics& cstat = _batchStatistics[i];
      G2OBatchStatistics::setGlobalStats(&cstat);
      cstat.iteration = i;
      cstat.numEdges = _activeEdges.size();
      cstat.numVertices = _activeVertices.size();
    }
 
    double ts = get_monotonic_time();
    result = _algorithm->solve(i, online);
    ok = (result == OptimizationAlgorithm::OK);
 
    bool errorComputed = false;
    if (_computeBatchStatistics) {
      computeActiveErrors();
      errorComputed = true;
      _batchStatistics[i].chi2 = activeRobustChi2();
      _batchStatistics[i].timeIteration = get_monotonic_time() - ts;
    }
 
    if (verbose()) {
      double dts = get_monotonic_time() - ts;
      cumTime += dts;
      if (!errorComputed) computeActiveErrors();
      cerr << "iteration= " << i << "\t chi2= " << FIXED(activeRobustChi2())
           << "\t time= " << dts << "\t cumTime= " << cumTime
           << "\t edges= " << _activeEdges.size();
      _algorithm->printVerbose(cerr);
      cerr << endl;
    }
    ++cjIterations;
    postIteration(i);
  }
  if (result == OptimizationAlgorithm::Fail) {
    return 0;
  }
  return cjIterations;
}
 
void SparseOptimizer::update(const double* update) {
  // update the graph by calling oplus on the vertices
  for (size_t i = 0; i < _ivMap.size(); ++i) {
    OptimizableGraph::Vertex* v = _ivMap[i];
#ifndef NDEBUG
    bool hasNan = arrayHasNaN(update, v->dimension());
    if (hasNan)
      G2O_WARN("{}: Update contains a nan for vertex {}", __PRETTY_FUNCTION__,
               v->id());
#endif
    v->oplus(update);
    update += v->dimension();
  }
}
 
void SparseOptimizer::setComputeBatchStatistics(bool computeBatchStatistics) {
  if ((_computeBatchStatistics == true) && (computeBatchStatistics == false)) {
    G2OBatchStatistics::setGlobalStats(0);
    _batchStatistics.clear();
  }
  _computeBatchStatistics = computeBatchStatistics;
}
 
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()) {
    G2O_ERROR("{}: something went wrong, size mismatch {} != {}",
              __PRETTY_FUNCTION__, vset.size(),
              newVertices.size());
  }
  return _algorithm->updateStructure(newVertices, eset);
}
 
void SparseOptimizer::sortVectorContainers() {
  // sort vector structures to get deterministic ordering based on IDs
  sort(_activeVertices.begin(), _activeVertices.end(), VertexIDCompare());
  sort(_activeEdges.begin(), _activeEdges.end(), EdgeIDCompare());
}
 
void SparseOptimizer::clear() {
  _ivMap.clear();
  _activeVertices.clear();
  _activeEdges.clear();
  OptimizableGraph::clear();
}
 
SparseOptimizer::VertexContainer::const_iterator
SparseOptimizer::findActiveVertex(const OptimizableGraph::Vertex* v) const {
  VertexContainer::const_iterator lower = lower_bound(
      _activeVertices.begin(), _activeVertices.end(), v, VertexIDCompare());
  if (lower == _activeVertices.end()) return _activeVertices.end();
  if ((*lower) == v) return lower;
  return _activeVertices.end();
}
 
SparseOptimizer::EdgeContainer::const_iterator SparseOptimizer::findActiveEdge(
    const OptimizableGraph::Edge* e) const {
  EdgeContainer::const_iterator lower =
      lower_bound(_activeEdges.begin(), _activeEdges.end(), e, EdgeIDCompare());
  if (lower == _activeEdges.end()) return _activeEdges.end();
  if ((*lower) == e) return lower;
  return _activeEdges.end();
}
 
void SparseOptimizer::push(SparseOptimizer::VertexContainer& vlist) {
  for (VertexContainer::iterator it = vlist.begin(); it != vlist.end(); ++it)
    (*it)->push();
}
 
void SparseOptimizer::pop(SparseOptimizer::VertexContainer& vlist) {
  for (VertexContainer::iterator it = vlist.begin(); it != vlist.end(); ++it)
    (*it)->pop();
}
 
void SparseOptimizer::push(HyperGraph::VertexSet& vlist) {
  OptimizableGraph::push(vlist);
}
 
void SparseOptimizer::pop(HyperGraph::VertexSet& vlist) {
  OptimizableGraph::pop(vlist);
}
 
void SparseOptimizer::discardTop(SparseOptimizer::VertexContainer& vlist) {
  for (VertexContainer::iterator it = vlist.begin(); it != vlist.end(); ++it)
    (*it)->discardTop();
}
 
void SparseOptimizer::setVerbose(bool verbose) { _verbose = verbose; }
 
void SparseOptimizer::setAlgorithm(OptimizationAlgorithm* algorithm) {
  if (_algorithm) {
    // reset the optimizer for the formerly used solver in case release() won't
    // delete it
    _algorithm->setOptimizer(nullptr);
    release(_algorithm);
  }
 
  _algorithm = algorithm;
 
  if (_algorithm) _algorithm->setOptimizer(this);
}
 
bool SparseOptimizer::computeMarginals(
    SparseBlockMatrix<MatrixX>& spinv,
    const std::vector<std::pair<int, int> >& blockIndices) {
  return _algorithm->computeMarginals(spinv, blockIndices);
}
 
bool SparseOptimizer::computeMarginals(SparseBlockMatrix<MatrixX>& spinv,
                                       const Vertex* vertex) {
  if (vertex->hessianIndex() < 0) {
    return false;
  }
  std::vector<std::pair<int, int> > index{
      std::make_pair<int, int>(vertex->hessianIndex(), vertex->hessianIndex())};
  return computeMarginals(spinv, index);
}
 
bool SparseOptimizer::computeMarginals(SparseBlockMatrix<MatrixX>& spinv,
                                       const VertexContainer& vertices) {
  std::vector<std::pair<int, int> > indices;
  indices.reserve(vertices.size());
  for (VertexContainer::const_iterator it = vertices.begin();
       it != vertices.end(); ++it) {
    indices.emplace_back((*it)->hessianIndex(), (*it)->hessianIndex());
  }
  return computeMarginals(spinv, indices);
}
 
void SparseOptimizer::setForceStopFlag(bool* flag) { _forceStopFlag = flag; }
 
bool SparseOptimizer::removeVertex(HyperGraph::Vertex* v, bool detach) {
  OptimizableGraph::Vertex* vv = static_cast<OptimizableGraph::Vertex*>(v);
  if (vv->hessianIndex() >= 0) {
    clearIndexMapping();
    _ivMap.clear();
  }
  return HyperGraph::removeVertex(v, detach);
}
 
bool SparseOptimizer::addComputeErrorAction(HyperGraphAction* action) {
  std::pair<HyperGraphActionSet::iterator, bool> insertResult =
      _graphActions[AT_COMPUTEACTIVERROR].insert(action);
  return insertResult.second;
}
 
bool SparseOptimizer::removeComputeErrorAction(HyperGraphAction* action) {
  return _graphActions[AT_COMPUTEACTIVERROR].erase(action) > 0;
}
 
void SparseOptimizer::push() { push(_activeVertices); }
 
void SparseOptimizer::pop() { pop(_activeVertices); }
 
void SparseOptimizer::discardTop() { discardTop(_activeVertices); }
 
}  // namespace g2o