void HyperDijkstra::connectedSubset(HyperGraph::VertexSet& connected, HyperGraph::VertexSet& visited,
HyperGraph::VertexSet& startingSet,
HyperGraph* g, HyperGraph::Vertex* v,
HyperDijkstra::CostFunction* cost, double distance,
double comparisonConditioner, double maxEdgeCost)
{
typedef std::queue<HyperGraph::Vertex*> VertexDeque;
visited.clear();
connected.clear();
VertexDeque frontier;
HyperDijkstra dv(g);
connected.insert(v);
frontier.push(v);
while (! frontier.empty()) {
HyperGraph::Vertex* v0=frontier.front();
frontier.pop();
dv.shortestPaths(v0, cost, distance, comparisonConditioner, false, maxEdgeCost);
for (HyperGraph::VertexSet::iterator it=dv.visited().begin(); it!=dv.visited().end(); ++it) {
visited.insert(*it);
if (startingSet.find(*it)==startingSet.end())
continue;
std::pair<HyperGraph::VertexSet::iterator, bool> insertOutcome=connected.insert(*it);
if (insertOutcome.second) { // the node was not in the connectedSet;
frontier.push(dynamic_cast<HyperGraph::Vertex*>(*it));
}
}
}
}
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 HyperDijkstra::shortestPaths(HyperGraph::Vertex* v, HyperDijkstra::CostFunction* cost, double maxDistance,
double comparisonConditioner, bool directed, double maxEdgeCost)
{
HyperGraph::VertexSet vset;
vset.insert(v);
shortestPaths(vset, cost, maxDistance, comparisonConditioner, directed, maxEdgeCost);
}
bool saveGnuplot(const std::string& gnudump, const OptimizableGraph& optimizer)
{
HyperGraph::VertexSet vset;
for (HyperGraph::VertexIDMap::const_iterator it=optimizer.vertices().begin(); it!=optimizer.vertices().end(); it++){
vset.insert(it->second);
}
return saveGnuplot(gnudump, vset, optimizer.edges());
}
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);
}
int main(int argc, char** argv) {
CommandArgs arg;
std::string outputFilename;
std::string inputFilename;
arg.param("o", outputFilename, "", "output file name");
arg.paramLeftOver("input-filename ", inputFilename, "", "graph file to read", true);
arg.parseArgs(argc, argv);
OptimizableGraph graph;
if (!graph.load(inputFilename.c_str())){
cerr << "Error: cannot load a file from \"" << inputFilename << "\", aborting." << endl;
return 0;
}
HyperGraph::EdgeSet removedEdges;
HyperGraph::VertexSet removedVertices;
for (HyperGraph::EdgeSet::iterator it = graph.edges().begin(); it!=graph.edges().end(); it++) {
HyperGraph::Edge* e = *it;
EdgeSE2PointXY* edgePointXY = dynamic_cast<EdgeSE2PointXY*>(e);
if (edgePointXY) {
VertexSE2* pose = dynamic_cast<VertexSE2*>(edgePointXY->vertex(0));
VertexPointXY* landmark = dynamic_cast<VertexPointXY*>(edgePointXY->vertex(1));
FeaturePointXYData * feature = new FeaturePointXYData();
feature->setPositionMeasurement(edgePointXY->measurement());
feature->setPositionInformation(edgePointXY->information());
pose->addUserData(feature);
removedEdges.insert(edgePointXY);
removedVertices.insert(landmark);
}
}
for (HyperGraph::EdgeSet::iterator it = removedEdges.begin(); it!=removedEdges.end(); it++){
OptimizableGraph::Edge* e = dynamic_cast<OptimizableGraph::Edge*>(*it);
graph.removeEdge(e);
}
for (HyperGraph::VertexSet::iterator it = removedVertices.begin(); it!=removedVertices.end(); it++){
OptimizableGraph::Vertex* v = dynamic_cast<OptimizableGraph::Vertex*>(*it);
graph.removeVertex(v);
}
if (outputFilename.length()){
graph.save(outputFilename.c_str());
}
}
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;
}
int main(int argc, char** argv)
{
OptimizableGraph::initMultiThreading();
int maxIterations;
bool verbose;
string inputFilename;
string gnudump;
string outputfilename;
string solverProperties;
string strSolver;
string loadLookup;
bool initialGuess;
bool initialGuessOdometry;
bool marginalize;
bool listTypes;
bool listSolvers;
bool listRobustKernels;
bool incremental;
bool guiOut;
int gaugeId;
string robustKernel;
bool computeMarginals;
bool printSolverProperties;
double huberWidth;
double gain;
int maxIterationsWithGain;
//double lambdaInit;
int updateGraphEachN = 10;
string statsFile;
string summaryFile;
bool nonSequential;
// command line parsing
std::vector<int> gaugeList;
CommandArgs arg;
arg.param("i", maxIterations, 5, "perform n iterations, if negative consider the gain");
arg.param("gain", gain, 1e-6, "the gain used to stop optimization (default = 1e-6)");
arg.param("ig",maxIterationsWithGain, std::numeric_limits<int>::max(), "Maximum number of iterations with gain enabled (default: inf)");
arg.param("v", verbose, false, "verbose output of the optimization process");
arg.param("guess", initialGuess, false, "initial guess based on spanning tree");
arg.param("guessOdometry", initialGuessOdometry, false, "initial guess based on odometry");
arg.param("inc", incremental, false, "run incremetally");
arg.param("update", updateGraphEachN, 10, "updates after x odometry nodes");
arg.param("guiout", guiOut, false, "gui output while running incrementally");
arg.param("marginalize", marginalize, false, "on or off");
arg.param("printSolverProperties", printSolverProperties, false, "print the properties of the solver");
arg.param("solverProperties", solverProperties, "", "set the internal properties of a solver,\n\te.g., initialLambda=0.0001,maxTrialsAfterFailure=2");
arg.param("gnudump", gnudump, "", "dump to gnuplot data file");
arg.param("robustKernel", robustKernel, "", "use this robust error function");
arg.param("robustKernelWidth", huberWidth, -1., "width for the robust Kernel (only if robustKernel)");
arg.param("computeMarginals", computeMarginals, false, "computes the marginal covariances of something. FOR TESTING ONLY");
arg.param("gaugeId", gaugeId, -1, "force the gauge");
arg.param("o", outputfilename, "", "output final version of the graph");
arg.param("solver", strSolver, "gn_var", "specify which solver to use underneat\n\t {gn_var, lm_fix3_2, gn_fix6_3, lm_fix7_3}");
#ifndef G2O_DISABLE_DYNAMIC_LOADING_OF_LIBRARIES
string dummy;
arg.param("solverlib", dummy, "", "specify a solver library which will be loaded");
arg.param("typeslib", dummy, "", "specify a types library which will be loaded");
#endif
arg.param("stats", statsFile, "", "specify a file for the statistics");
arg.param("listTypes", listTypes, false, "list the registered types");
arg.param("listRobustKernels", listRobustKernels, false, "list the registered robust kernels");
arg.param("listSolvers", listSolvers, false, "list the available solvers");
arg.param("renameTypes", loadLookup, "", "create a lookup for loading types into other types,\n\t TAG_IN_FILE=INTERNAL_TAG_FOR_TYPE,TAG2=INTERNAL2\n\t e.g., VERTEX_CAM=VERTEX_SE3:EXPMAP");
arg.param("gaugeList", gaugeList, std::vector<int>(), "set the list of gauges separated by commas without spaces \n e.g: 1,2,3,4,5 ");
arg.param("summary", summaryFile, "", "append a summary of this optimization run to the summary file passed as argument");
arg.paramLeftOver("graph-input", inputFilename, "", "graph file which will be processed", true);
arg.param("nonSequential", nonSequential, false, "apply the robust kernel only on loop closures and not odometries");
arg.parseArgs(argc, argv);
if (verbose) {
cout << "# Used Compiler: " << G2O_CXX_COMPILER << endl;
}
#ifndef G2O_DISABLE_DYNAMIC_LOADING_OF_LIBRARIES
// registering all the types from the libraries
DlWrapper dlTypesWrapper;
loadStandardTypes(dlTypesWrapper, argc, argv);
// register all the solvers
DlWrapper dlSolverWrapper;
loadStandardSolver(dlSolverWrapper, argc, argv);
#else
if (verbose)
cout << "# linked version of g2o" << endl;
#endif
OptimizationAlgorithmFactory* solverFactory = OptimizationAlgorithmFactory::instance();
if (listSolvers) {
solverFactory->listSolvers(cout);
}
if (listTypes) {
Factory::instance()->printRegisteredTypes(cout, true);
}
if (listRobustKernels) {
std::vector<std::string> kernels;
RobustKernelFactory::instance()->fillKnownKernels(kernels);
cout << "Robust Kernels:" << endl;
for (size_t i = 0; i < kernels.size(); ++i) {
cout << kernels[i] << endl;
}
}
SparseOptimizer optimizer;
optimizer.setVerbose(verbose);
optimizer.setForceStopFlag(&hasToStop);
SparseOptimizerTerminateAction* terminateAction = 0;
if (maxIterations < 0) {
cerr << "# setup termination criterion based on the gain of the iteration" << endl;
maxIterations = maxIterationsWithGain;
terminateAction = new SparseOptimizerTerminateAction;
terminateAction->setGainThreshold(gain);
terminateAction->setMaxIterations(maxIterationsWithGain);
optimizer.addPostIterationAction(terminateAction);
}
// allocating the desired solver + testing whether the solver is okay
OptimizationAlgorithmProperty solverProperty;
optimizer.setAlgorithm(solverFactory->construct(strSolver, solverProperty));
if (! optimizer.solver()) {
cerr << "Error allocating solver. Allocating \"" << strSolver << "\" failed!" << endl;
return 0;
}
if (solverProperties.size() > 0) {
bool updateStatus = optimizer.solver()->updatePropertiesFromString(solverProperties);
if (! updateStatus) {
cerr << "Failure while updating the solver properties from the given string" << endl;
}
}
if (solverProperties.size() > 0 || printSolverProperties) {
optimizer.solver()->printProperties(cerr);
}
// Loading the input data
if (loadLookup.size() > 0) {
optimizer.setRenamedTypesFromString(loadLookup);
}
if (inputFilename.size() == 0) {
cerr << "No input data specified" << endl;
return 0;
} else if (inputFilename == "-") {
cerr << "Read input from stdin" << endl;
if (!optimizer.load(cin)) {
cerr << "Error loading graph" << endl;
return 2;
}
} else {
cerr << "Read input from " << inputFilename << endl;
ifstream ifs(inputFilename.c_str());
if (!ifs) {
cerr << "Failed to open file" << endl;
return 1;
}
if (!optimizer.load(ifs)) {
cerr << "Error loading graph" << endl;
return 2;
}
}
cerr << "Loaded " << optimizer.vertices().size() << " vertices" << endl;
cerr << "Loaded " << optimizer.edges().size() << " edges" << endl;
if (optimizer.vertices().size() == 0) {
cerr << "Graph contains no vertices" << endl;
return 1;
}
set<int> vertexDimensions = optimizer.dimensions();
if (! optimizer.isSolverSuitable(solverProperty, vertexDimensions)) {
cerr << "The selected solver is not suitable for optimizing the given graph" << endl;
return 3;
}
assert (optimizer.solver());
//optimizer.setMethod(str2method(strMethod));
//optimizer.setUserLambdaInit(lambdaInit);
// check for vertices to fix to remove DoF
bool gaugeFreedom = optimizer.gaugeFreedom();
OptimizableGraph::Vertex* gauge=0;
if (gaugeList.size()){
cerr << "Fixing gauges: ";
for (size_t i=0; i<gaugeList.size(); i++){
int id=gaugeList[i];
OptimizableGraph::Vertex* v=optimizer.vertex(id);
if (!v){
cerr << "fatal, not found the vertex of id " << id << " in the gaugeList. Aborting";
return -1;
} else {
if (i==0)
gauge = v;
cerr << v->id() << " ";
v->setFixed(1);
}
}
cerr << endl;
gaugeFreedom = false;
} else {
gauge=optimizer.findGauge();
}
if (gaugeFreedom) {
if (! gauge) {
cerr << "# cannot find a vertex to fix in this thing" << endl;
return 2;
} else {
cerr << "# graph is fixed by node " << gauge->id() << endl;
gauge->setFixed(true);
}
} else {
cerr << "# graph is fixed by priors or already fixed vertex" << endl;
}
// if schur, we wanna marginalize the landmarks...
if (marginalize || solverProperty.requiresMarginalize) {
int maxDim = *vertexDimensions.rbegin();
int minDim = *vertexDimensions.begin();
if (maxDim != minDim) {
cerr << "# Preparing Marginalization of the Landmarks ... ";
for (HyperGraph::VertexIDMap::iterator it=optimizer.vertices().begin(); it!=optimizer.vertices().end(); it++){
OptimizableGraph::Vertex* v=static_cast<OptimizableGraph::Vertex*>(it->second);
if (v->dimension() != maxDim) {
v->setMarginalized(true);
}
}
cerr << "done." << endl;
}
}
if (robustKernel.size() > 0) {
AbstractRobustKernelCreator* creator = RobustKernelFactory::instance()->creator(robustKernel);
cerr << "# Preparing robust error function ... ";
if (creator) {
if (nonSequential) {
for (SparseOptimizer::EdgeSet::iterator it = optimizer.edges().begin(); it != optimizer.edges().end(); ++it) {
SparseOptimizer::Edge* e = dynamic_cast<SparseOptimizer::Edge*>(*it);
if (e->vertices().size() >= 2 && std::abs(e->vertex(0)->id() - e->vertex(1)->id()) != 1) {
e->setRobustKernel(creator->construct());
if (huberWidth > 0)
e->robustKernel()->setDelta(huberWidth);
}
}
} else {
for (SparseOptimizer::EdgeSet::iterator it = optimizer.edges().begin(); it != optimizer.edges().end(); ++it) {
SparseOptimizer::Edge* e = dynamic_cast<SparseOptimizer::Edge*>(*it);
e->setRobustKernel(creator->construct());
if (huberWidth > 0)
e->robustKernel()->setDelta(huberWidth);
}
}
cerr << "done." << endl;
} else {
cerr << "Unknown Robust Kernel: " << robustKernel << endl;
}
}
// sanity check
HyperDijkstra d(&optimizer);
UniformCostFunction f;
d.shortestPaths(gauge,&f);
//cerr << PVAR(d.visited().size()) << endl;
if (d.visited().size()!=optimizer.vertices().size()) {
cerr << CL_RED("Warning: d.visited().size() != optimizer.vertices().size()") << endl;
cerr << "visited: " << d.visited().size() << endl;
cerr << "vertices: " << optimizer.vertices().size() << endl;
}
if (incremental) {
cerr << CL_RED("# Note: this variant performs batch steps in each time step") << endl;
cerr << CL_RED("# For a variant which updates the Cholesky factor use the binary g2o_incremental") << endl;
int incIterations = maxIterations;
if (! arg.parsedParam("i")) {
cerr << "# Setting default number of iterations" << endl;
incIterations = 1;
}
int updateDisplayEveryN = updateGraphEachN;
int maxDim = 0;
cerr << "# incremental settings" << endl;
cerr << "#\t solve every " << updateGraphEachN << endl;
cerr << "#\t iterations " << incIterations << endl;
SparseOptimizer::VertexIDMap vertices = optimizer.vertices();
for (SparseOptimizer::VertexIDMap::const_iterator it = vertices.begin(); it != vertices.end(); ++it) {
const SparseOptimizer::Vertex* v = static_cast<const SparseOptimizer::Vertex*>(it->second);
maxDim = max(maxDim, v->dimension());
}
vector<SparseOptimizer::Edge*> edges;
for (SparseOptimizer::EdgeSet::iterator it = optimizer.edges().begin(); it != optimizer.edges().end(); ++it) {
SparseOptimizer::Edge* e = dynamic_cast<SparseOptimizer::Edge*>(*it);
edges.push_back(e);
}
optimizer.edges().clear();
optimizer.vertices().clear();
optimizer.setVerbose(false);
// sort the edges in a way that inserting them makes sense
sort(edges.begin(), edges.end(), IncrementalEdgesCompare());
double cumTime = 0.;
int vertexCount=0;
int lastOptimizedVertexCount = 0;
int lastVisUpdateVertexCount = 0;
bool freshlyOptimized=false;
bool firstRound = true;
HyperGraph::VertexSet verticesAdded;
HyperGraph::EdgeSet edgesAdded;
for (vector<SparseOptimizer::Edge*>::iterator it = edges.begin(); it != edges.end(); ++it) {
SparseOptimizer::Edge* e = *it;
int doInit = 0;
SparseOptimizer::Vertex* v1 = optimizer.vertex(e->vertices()[0]->id());
SparseOptimizer::Vertex* v2 = optimizer.vertex(e->vertices()[1]->id());
if (! v1) {
SparseOptimizer::Vertex* v = v1 = dynamic_cast<SparseOptimizer::Vertex*>(e->vertices()[0]);
bool v1Added = optimizer.addVertex(v);
//cerr << "adding" << v->id() << "(" << v->dimension() << ")" << endl;
assert(v1Added);
if (! v1Added)
cerr << "Error adding vertex " << v->id() << endl;
else
verticesAdded.insert(v);
doInit = 1;
if (v->dimension() == maxDim)
vertexCount++;
}
if (! v2) {
SparseOptimizer::Vertex* v = v2 = dynamic_cast<SparseOptimizer::Vertex*>(e->vertices()[1]);
bool v2Added = optimizer.addVertex(v);
//cerr << "adding" << v->id() << "(" << v->dimension() << ")" << endl;
assert(v2Added);
if (! v2Added)
cerr << "Error adding vertex " << v->id() << endl;
else
verticesAdded.insert(v);
doInit = 2;
if (v->dimension() == maxDim)
vertexCount++;
}
// adding the edge and initialization of the vertices
{
//cerr << " adding edge " << e->vertices()[0]->id() << " " << e->vertices()[1]->id() << endl;
if (! optimizer.addEdge(e)) {
cerr << "Unable to add edge " << e->vertices()[0]->id() << " -> " << e->vertices()[1]->id() << endl;
} else {
edgesAdded.insert(e);
}
if (doInit) {
OptimizableGraph::Vertex* from = static_cast<OptimizableGraph::Vertex*>(e->vertices()[0]);
OptimizableGraph::Vertex* to = static_cast<OptimizableGraph::Vertex*>(e->vertices()[1]);
switch (doInit){
case 1: // initialize v1 from v2
{
HyperGraph::VertexSet toSet;
toSet.insert(to);
if (e->initialEstimatePossible(toSet, from) > 0.) {
//cerr << "init: "
//<< to->id() << "(" << to->dimension() << ") -> "
//<< from->id() << "(" << from->dimension() << ") " << endl;
e->initialEstimate(toSet, from);
} else {
assert(0 && "Added unitialized variable to the graph");
}
break;
}
case 2:
{
HyperGraph::VertexSet fromSet;
fromSet.insert(from);
if (e->initialEstimatePossible(fromSet, to) > 0.) {
//cerr << "init: "
//<< from->id() << "(" << from->dimension() << ") -> "
//<< to->id() << "(" << to->dimension() << ") " << endl;
e->initialEstimate(fromSet, to);
} else {
assert(0 && "Added unitialized variable to the graph");
}
break;
}
default: cerr << "doInit wrong value\n";
}
}
}
freshlyOptimized=false;
{
//cerr << "Optimize" << endl;
if (vertexCount - lastOptimizedVertexCount >= updateGraphEachN) {
if (firstRound) {
if (!optimizer.initializeOptimization()){
cerr << "initialization failed" << endl;
return 0;
}
} else {
if (! optimizer.updateInitialization(verticesAdded, edgesAdded)) {
cerr << "updating initialization failed" << endl;
return 0;
}
}
verticesAdded.clear();
edgesAdded.clear();
double ts = get_monotonic_time();
int currentIt=optimizer.optimize(incIterations, !firstRound);
double dts = get_monotonic_time() - ts;
cumTime += dts;
firstRound = false;
//optimizer->setOptimizationTime(cumTime);
if (verbose) {
double chi2 = optimizer.chi2();
cerr << "nodes= " << optimizer.vertices().size() << "\t edges= " << optimizer.edges().size() << "\t chi2= " << chi2
<< "\t time= " << dts << "\t iterations= " << currentIt << "\t cumTime= " << cumTime << endl;
}
lastOptimizedVertexCount = vertexCount;
freshlyOptimized = true;
if (guiOut) {
if (vertexCount - lastVisUpdateVertexCount >= updateDisplayEveryN) {
dumpEdges(cout, optimizer);
lastVisUpdateVertexCount = vertexCount;
}
}
}
if (! verbose)
cerr << ".";
}
} // for all edges
if (! freshlyOptimized) {
double ts = get_monotonic_time();
int currentIt=optimizer.optimize(incIterations, !firstRound);
double dts = get_monotonic_time() - ts;
cumTime += dts;
//optimizer->setOptimizationTime(cumTime);
if (verbose) {
double chi2 = optimizer.chi2();
cerr << "nodes= " << optimizer.vertices().size() << "\t edges= " << optimizer.edges().size() << "\t chi2= " << chi2
<< "\t time= " << dts << "\t iterations= " << currentIt << "\t cumTime= " << cumTime << endl;
}
}
} else {
// BATCH optimization
if (statsFile!=""){
// allocate buffer for statistics;
optimizer.setComputeBatchStatistics(true);
}
optimizer.initializeOptimization();
optimizer.computeActiveErrors();
double loadChi = optimizer.chi2();
cerr << "Initial chi2 = " << FIXED(loadChi) << endl;
if (initialGuess) {
optimizer.computeInitialGuess();
} else if (initialGuessOdometry) {
EstimatePropagatorCostOdometry costFunction(&optimizer);
optimizer.computeInitialGuess(costFunction);
}
double initChi = optimizer.chi2();
signal(SIGINT, sigquit_handler);
int result=optimizer.optimize(maxIterations);
if (maxIterations > 0 && result==OptimizationAlgorithm::Fail){
cerr << "Cholesky failed, result might be invalid" << endl;
} else if (computeMarginals){
std::vector<std::pair<int, int> > blockIndices;
for (size_t i=0; i<optimizer.activeVertices().size(); i++){
OptimizableGraph::Vertex* v=optimizer.activeVertices()[i];
if (v->hessianIndex()>=0){
blockIndices.push_back(make_pair(v->hessianIndex(), v->hessianIndex()));
}
if (v->hessianIndex()>0){
blockIndices.push_back(make_pair(v->hessianIndex()-1, v->hessianIndex()));
}
}
SparseBlockMatrix<MatrixXd> spinv;
if (optimizer.computeMarginals(spinv, blockIndices)) {
for (size_t i=0; i<optimizer.activeVertices().size(); i++){
OptimizableGraph::Vertex* v=optimizer.activeVertices()[i];
cerr << "Vertex id:" << v->id() << endl;
if (v->hessianIndex()>=0){
cerr << "inv block :" << v->hessianIndex() << ", " << v->hessianIndex()<< endl;
cerr << *(spinv.block(v->hessianIndex(), v->hessianIndex()));
cerr << endl;
}
if (v->hessianIndex()>0){
cerr << "inv block :" << v->hessianIndex()-1 << ", " << v->hessianIndex()<< endl;
cerr << *(spinv.block(v->hessianIndex()-1, v->hessianIndex()));
cerr << endl;
}
}
}
}
optimizer.computeActiveErrors();
double finalChi=optimizer.chi2();
if (summaryFile!="") {
PropertyMap summary;
summary.makeProperty<StringProperty>("filename", inputFilename);
summary.makeProperty<IntProperty>("n_vertices", optimizer.vertices().size());
summary.makeProperty<IntProperty>("n_edges", optimizer.edges().size());
int nLandmarks=0;
int nPoses=0;
int maxDim = *vertexDimensions.rbegin();
for (HyperGraph::VertexIDMap::iterator it=optimizer.vertices().begin(); it!=optimizer.vertices().end(); it++){
OptimizableGraph::Vertex* v=static_cast<OptimizableGraph::Vertex*>(it->second);
if (v->dimension() != maxDim) {
nLandmarks++;
} else
nPoses++;
}
set<string> edgeTypes;
for (HyperGraph::EdgeSet::iterator it=optimizer.edges().begin(); it!=optimizer.edges().end(); it++){
edgeTypes.insert(Factory::instance()->tag(*it));
}
stringstream edgeTypesString;
for (std::set<string>::iterator it=edgeTypes.begin(); it!=edgeTypes.end(); it++){
edgeTypesString << *it << " ";
}
summary.makeProperty<IntProperty>("n_poses", nPoses);
summary.makeProperty<IntProperty>("n_landmarks", nLandmarks);
summary.makeProperty<StringProperty>("edge_types", edgeTypesString.str());
summary.makeProperty<DoubleProperty>("load_chi", loadChi);
summary.makeProperty<StringProperty>("solver", strSolver);
summary.makeProperty<BoolProperty>("robustKernel", robustKernel.size() > 0);
summary.makeProperty<DoubleProperty>("init_chi", initChi);
summary.makeProperty<DoubleProperty>("final_chi", finalChi);
summary.makeProperty<IntProperty>("maxIterations", maxIterations);
summary.makeProperty<IntProperty>("realIterations", result);
ofstream os;
os.open(summaryFile.c_str(), ios::app);
summary.writeToCSV(os);
}
if (statsFile!=""){
cerr << "writing stats to file \"" << statsFile << "\" ... ";
ofstream os(statsFile.c_str());
const BatchStatisticsContainer& bsc = optimizer.batchStatistics();
for (int i=0; i<maxIterations; i++) {
os << bsc[i] << endl;
}
cerr << "done." << endl;
}
}
// saving again
if (gnudump.size() > 0) {
bool gnuPlotStatus = saveGnuplot(gnudump, optimizer);
if (! gnuPlotStatus) {
cerr << "Error while writing gnuplot files" << endl;
}
}
if (outputfilename.size() > 0) {
if (outputfilename == "-") {
cerr << "saving to stdout";
optimizer.save(cout);
} else {
cerr << "saving " << outputfilename << " ... ";
optimizer.save(outputfilename.c_str());
}
cerr << "done." << endl;
}
// destroy all the singletons
//Factory::destroy();
//OptimizationAlgorithmFactory::destroy();
//HyperGraphActionLibrary::destroy();
return 0;
}
bool G2oSlamInterface::addEdge(const std::string& tag, int id, int dimension, int v1Id, int v2Id, const std::vector<double>& measurement, const std::vector<double>& information)
{
(void) tag;
(void) id;
size_t oldEdgesSize = _optimizer->edges().size();
if (dimension == 3) {
SE2 transf(measurement[0], measurement[1], measurement[2]);
Eigen::Matrix3d infMat;
int idx = 0;
for (int r = 0; r < 3; ++r)
for (int c = r; c < 3; ++c, ++idx) {
assert(idx < (int)information.size());
infMat(r,c) = infMat(c,r) = information[idx];
}
//cerr << PVAR(infMat) << endl;
int doInit = 0;
SparseOptimizer::Vertex* v1 = _optimizer->vertex(v1Id);
SparseOptimizer::Vertex* v2 = _optimizer->vertex(v2Id);
if (! v1) {
OptimizableGraph::Vertex* v = v1 = addVertex(dimension, v1Id);
_verticesAdded.insert(v);
doInit = 1;
++_nodesAdded;
}
if (! v2) {
OptimizableGraph::Vertex* v = v2 = addVertex(dimension, v2Id);
_verticesAdded.insert(v);
doInit = 2;
++_nodesAdded;
}
if (_optimizer->edges().size() == 0) {
cerr << "FIRST EDGE ";
if (v1->id() < v2->id()) {
cerr << "fixing " << v1->id() << endl;
v1->setFixed(true);
}
else {
cerr << "fixing " << v2->id() << endl;
v2->setFixed(true);
}
}
OnlineEdgeSE2* e = new OnlineEdgeSE2;
e->vertices()[0] = v1;
e->vertices()[1] = v2;
e->setMeasurement(transf);
e->setInformation(infMat);
_optimizer->addEdge(e);
_edgesAdded.insert(e);
if (doInit) {
OptimizableGraph::Vertex* from = static_cast<OptimizableGraph::Vertex*>(e->vertices()[0]);
OptimizableGraph::Vertex* to = static_cast<OptimizableGraph::Vertex*>(e->vertices()[1]);
switch (doInit){
case 1: // initialize v1 from v2
{
HyperGraph::VertexSet toSet;
toSet.insert(to);
if (e->initialEstimatePossible(toSet, from) > 0.) {
e->initialEstimate(toSet, from);
}
break;
}
case 2:
{
HyperGraph::VertexSet fromSet;
fromSet.insert(from);
if (e->initialEstimatePossible(fromSet, to) > 0.) {
e->initialEstimate(fromSet, to);
}
break;
}
default: cerr << "doInit wrong value\n";
}
}
}
else if (dimension == 6) {
Eigen::Isometry3d transf;
Matrix<double, 6, 6> infMat;
if (measurement.size() == 7) { // measurement is a Quaternion
Vector7d meas;
for (int i=0; i<7; ++i)
meas(i) = measurement[i];
// normalize the quaternion to recover numerical precision lost by storing as human readable text
Vector4d::MapType(meas.data()+3).normalize();
transf = internal::fromVectorQT(meas);
for (int i = 0, idx = 0; i < infMat.rows(); ++i)
for (int j = i; j < infMat.cols(); ++j){
infMat(i,j) = information[idx++];
if (i != j)
infMat(j,i)=infMat(i,j);
}
} else { // measurement consists of Euler angles
Vector6d aux;
aux << measurement[0], measurement[1], measurement[2],measurement[3], measurement[4], measurement[5];
transf = internal::fromVectorET(aux);
Matrix<double, 6, 6> infMatEuler;
int idx = 0;
for (int r = 0; r < 6; ++r)
for (int c = r; c < 6; ++c, ++idx) {
assert(idx < (int)information.size());
infMatEuler(r,c) = infMatEuler(c,r) = information[idx];
}
// convert information matrix to our internal representation
Matrix<double, 6, 6> J;
SE3Quat transfAsSe3(transf.matrix().topLeftCorner<3,3>(), transf.translation());
jac_quat3_euler3(J, transfAsSe3);
infMat.noalias() = J.transpose() * infMatEuler * J;
//cerr << PVAR(transf.matrix()) << endl;
//cerr << PVAR(infMat) << endl;
}
int doInit = 0;
SparseOptimizer::Vertex* v1 = _optimizer->vertex(v1Id);
SparseOptimizer::Vertex* v2 = _optimizer->vertex(v2Id);
if (! v1) {
OptimizableGraph::Vertex* v = v1 = addVertex(dimension, v1Id);
_verticesAdded.insert(v);
doInit = 1;
++_nodesAdded;
}
if (! v2) {
OptimizableGraph::Vertex* v = v2 = addVertex(dimension, v2Id);
_verticesAdded.insert(v);
doInit = 2;
++_nodesAdded;
}
if (_optimizer->edges().size() == 0) {
cerr << "FIRST EDGE ";
if (v1->id() < v2->id()) {
cerr << "fixing " << v1->id() << endl;
v1->setFixed(true);
}
else {
cerr << "fixing " << v2->id() << endl;
v2->setFixed(true);
}
}
OnlineEdgeSE3* e = new OnlineEdgeSE3;
e->vertices()[0] = v1;
e->vertices()[1] = v2;
e->setMeasurement(transf);
e->setInformation(infMat);
_optimizer->addEdge(e);
_edgesAdded.insert(e);
if (doInit) {
OptimizableGraph::Vertex* from = static_cast<OptimizableGraph::Vertex*>(e->vertices()[0]);
OptimizableGraph::Vertex* to = static_cast<OptimizableGraph::Vertex*>(e->vertices()[1]);
switch (doInit){
case 1: // initialize v1 from v2
{
HyperGraph::VertexSet toSet;
toSet.insert(to);
if (e->initialEstimatePossible(toSet, from) > 0.) {
e->initialEstimate(toSet, from);
}
break;
}
case 2:
{
HyperGraph::VertexSet fromSet;
fromSet.insert(from);
if (e->initialEstimatePossible(fromSet, to) > 0.) {
e->initialEstimate(fromSet, to);
}
break;
}
default: cerr << "doInit wrong value\n";
}
}
}
else {
cerr << __PRETTY_FUNCTION__ << " not implemented for this dimension" << endl;
return false;
}
if (oldEdgesSize == 0) {
_optimizer->jacobianWorkspace().allocate();
}
return true;
}
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;
}
