inline void updateVertexIdx()
{
if ((ros::Time::now() - lastSavedNodeTime).toSec() >= 0.03)
{
nodeCounter++;
lastSavedNodeTime = ros::Time::now();
PreviousVertexId = CurrentVertexId;
CurrentVertexId++;
if (CurrentVertexId - LandmarkCount >= 100)
{
CurrentVertexId = LandmarkCount;
}
{
VertexSE2 * r = new VertexSE2;
r->setEstimate(Eigen::Vector3d(location.x, location.y, 0));
r->setFixed(false);
r->setId(CurrentVertexId);
if (optimizer.vertex(CurrentVertexId) != NULL)
{
optimizer.removeVertex(optimizer.vertex(CurrentVertexId));
}
optimizer.addVertex(r);
}
{
EdgeSE2 * e = new EdgeSE2;
e->vertices()[0] = optimizer.vertex(PreviousVertexId);
e->vertices()[1] = optimizer.vertex(CurrentVertexId);
Point2d dead_reck = getOdometryFromLastGet();
e->setMeasurement(SE2(dead_reck.x, dead_reck.y, 0));
Matrix3d information;
information.fill(0.);
information(0, 0) = 200;
information(1, 1) = 200;
information(2, 2) = 1;
e->setInformation(information);
optimizer.addEdge(e);
}
}
}
int main()
{
double euc_noise = 0.01; // noise in position, m
// double outlier_ratio = 0.1;
SparseOptimizer optimizer;
optimizer.setVerbose(false);
// variable-size block solver
BlockSolverX::LinearSolverType * linearSolver = new LinearSolverDense<g2o::BlockSolverX::PoseMatrixType>();
BlockSolverX * solver_ptr = new BlockSolverX(linearSolver);
g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
optimizer.setAlgorithm(solver);
vector<Vector3d> true_points;
for (size_t i=0;i<1000; ++i)
{
true_points.push_back(Vector3d((Sample::uniform()-0.5)*3,
Sample::uniform()-0.5,
Sample::uniform()+10));
}
// set up two poses
int vertex_id = 0;
for (size_t i=0; i<2; ++i)
{
// set up rotation and translation for this node
Vector3d t(0,0,i);
Quaterniond q;
q.setIdentity();
Eigen::Isometry3d cam; // camera pose
cam = q;
cam.translation() = t;
// set up node
VertexSE3 *vc = new VertexSE3();
vc->setEstimate(cam);
vc->setId(vertex_id); // vertex id
cerr << t.transpose() << " | " << q.coeffs().transpose() << endl;
// set first cam pose fixed
if (i==0)
vc->setFixed(true);
// add to optimizer
optimizer.addVertex(vc);
vertex_id++;
}
// set up point matches
for (size_t i=0; i<true_points.size(); ++i)
{
// get two poses
VertexSE3* vp0 =
dynamic_cast<VertexSE3*>(optimizer.vertices().find(0)->second);
VertexSE3* vp1 =
dynamic_cast<VertexSE3*>(optimizer.vertices().find(1)->second);
// calculate the relative 3D position of the point
Vector3d pt0,pt1;
pt0 = vp0->estimate().inverse() * true_points[i];
pt1 = vp1->estimate().inverse() * true_points[i];
// add in noise
pt0 += Vector3d(Sample::gaussian(euc_noise ),
Sample::gaussian(euc_noise ),
Sample::gaussian(euc_noise ));
pt1 += Vector3d(Sample::gaussian(euc_noise ),
Sample::gaussian(euc_noise ),
Sample::gaussian(euc_noise ));
// form edge, with normals in varioius positions
Vector3d nm0, nm1;
nm0 << 0, i, 1;
nm1 << 0, i, 1;
nm0.normalize();
nm1.normalize();
Edge_V_V_GICP * e // new edge with correct cohort for caching
= new Edge_V_V_GICP();
e->setVertex(0, vp0); // first viewpoint
e->setVertex(1, vp1); // second viewpoint
EdgeGICP meas;
meas.pos0 = pt0;
meas.pos1 = pt1;
meas.normal0 = nm0;
meas.normal1 = nm1;
e->setMeasurement(meas);
// e->inverseMeasurement().pos() = -kp;
meas = e->measurement();
// use this for point-plane
e->information() = meas.prec0(0.01);
// use this for point-point
// e->information().setIdentity();
// e->setRobustKernel(true);
//e->setHuberWidth(0.01);
optimizer.addEdge(e);
}
// move second cam off of its true position
VertexSE3* vc =
dynamic_cast<VertexSE3*>(optimizer.vertices().find(1)->second);
Eigen::Isometry3d cam = vc->estimate();
cam.translation() = Vector3d(0,0,0.2);
vc->setEstimate(cam);
optimizer.initializeOptimization();
optimizer.computeActiveErrors();
cout << "Initial chi2 = " << FIXED(optimizer.chi2()) << endl;
optimizer.setVerbose(true);
optimizer.optimize(5);
cout << endl << "Second vertex should be near 0,0,1" << endl;
cout << dynamic_cast<VertexSE3*>(optimizer.vertices().find(0)->second)
->estimate().translation().transpose() << endl;
cout << dynamic_cast<VertexSE3*>(optimizer.vertices().find(1)->second)
->estimate().translation().transpose() << endl;
}
int main(int argc, char** argv)
{
bool fixLaser;
int maxIterations;
bool verbose;
string inputFilename;
string outputfilename;
string rawFilename;
string odomTestFilename;
string dumpGraphFilename;
// command line parsing
CommandArgs commandLineArguments;
commandLineArguments.param("i", maxIterations, 10, "perform n iterations");
commandLineArguments.param("v", verbose, false, "verbose output of the optimization process");
commandLineArguments.param("o", outputfilename, "", "output final version of the graph");
commandLineArguments.param("test", odomTestFilename, "", "apply odometry calibration to some test data");
commandLineArguments.param("dump", dumpGraphFilename, "", "write the graph to the disk");
commandLineArguments.param("fixLaser", fixLaser, false, "keep the laser offset fixed during optimization");
commandLineArguments.paramLeftOver("gm2dl-input", inputFilename, "", "gm2dl file which will be processed");
commandLineArguments.paramLeftOver("raw-log", rawFilename, "", "raw log file containing the odometry");
commandLineArguments.parseArgs(argc, argv);
SparseOptimizer optimizer;
optimizer.setVerbose(verbose);
allocateSolverForSclam(optimizer);
// loading
DataQueue odometryQueue;
int numLaserOdom = Gm2dlIO::readRobotLaser(rawFilename, odometryQueue);
if (numLaserOdom == 0) {
cerr << "No raw information read" << endl;
return 0;
}
cerr << "Read " << numLaserOdom << " laser readings from file" << endl;
Eigen::Vector3d odomCalib(1., 1., 1.);
SE2 initialLaserPose;
DataQueue robotLaserQueue;
int numRobotLaser = Gm2dlIO::readRobotLaser(inputFilename, robotLaserQueue);
if (numRobotLaser == 0) {
cerr << "No robot laser read" << endl;
return 0;
} else {
RobotLaser* rl = dynamic_cast<RobotLaser*>(robotLaserQueue.buffer().begin()->second);
initialLaserPose = rl->odomPose().inverse() * rl->laserPose();
cerr << PVAR(initialLaserPose.toVector().transpose()) << endl;
}
// adding the measurements
vector<MotionInformation, Eigen::aligned_allocator<MotionInformation> > motions;
{
std::map<double, RobotData*>::const_iterator it = robotLaserQueue.buffer().begin();
std::map<double, RobotData*>::const_iterator prevIt = it++;
for (; it != robotLaserQueue.buffer().end(); ++it) {
MotionInformation mi;
RobotLaser* prevLaser = dynamic_cast<RobotLaser*>(prevIt->second);
RobotLaser* curLaser = dynamic_cast<RobotLaser*>(it->second);
mi.laserMotion = prevLaser->laserPose().inverse() * curLaser->laserPose();
// get the motion of the robot in that time interval
RobotLaser* prevOdom = dynamic_cast<RobotLaser*>(odometryQueue.findClosestData(prevLaser->timestamp()));
RobotLaser* curOdom = dynamic_cast<RobotLaser*>(odometryQueue.findClosestData(curLaser->timestamp()));
mi.odomMotion = prevOdom->odomPose().inverse() * curOdom->odomPose();
mi.timeInterval = prevOdom->timestamp() - curOdom->timestamp();
prevIt = it;
motions.push_back(mi);
}
}
if (1) {
VertexSE2* laserOffset = new VertexSE2;
laserOffset->setId(Gm2dlIO::ID_LASERPOSE);
laserOffset->setEstimate(initialLaserPose);
optimizer.addVertex(laserOffset);
VertexOdomDifferentialParams* odomParamsVertex = new VertexOdomDifferentialParams;
odomParamsVertex->setId(Gm2dlIO::ID_ODOMCALIB);
odomParamsVertex->setEstimate(Eigen::Vector3d(1., 1., 1.));
optimizer.addVertex(odomParamsVertex);
for (size_t i = 0; i < motions.size(); ++i) {
const SE2& odomMotion = motions[i].odomMotion;
const SE2& laserMotion = motions[i].laserMotion;
const double& timeInterval = motions[i].timeInterval;
// add the edge
MotionMeasurement mm(odomMotion.translation().x(), odomMotion.translation().y(), odomMotion.rotation().angle(), timeInterval);
OdomAndLaserMotion meas;
meas.velocityMeasurement = OdomConvert::convertToVelocity(mm);
meas.laserMotion = laserMotion;
EdgeSE2PureCalib* calibEdge = new EdgeSE2PureCalib;
calibEdge->setVertex(0, laserOffset);
calibEdge->setVertex(1, odomParamsVertex);
calibEdge->setInformation(Eigen::Matrix3d::Identity());
calibEdge->setMeasurement(meas);
if (! optimizer.addEdge(calibEdge)) {
cerr << "Error adding calib edge" << endl;
delete calibEdge;
}
}
if (fixLaser) {
cerr << "Fix position of the laser offset" << endl;
laserOffset->setFixed(true);
}
cerr << "\nPerforming full non-linear estimation" << endl;
optimizer.initializeOptimization();
optimizer.computeActiveErrors();
optimizer.optimize(maxIterations);
cerr << "Calibrated laser offset (x, y, theta):" << laserOffset->estimate().toVector().transpose() << endl;
odomCalib = odomParamsVertex->estimate();
cerr << "Odometry parameters (scaling factors (v_l, v_r, b)): " << odomParamsVertex->estimate().transpose() << endl;
optimizer.clear();
}
// linear least squares for some parameters
{
Eigen::MatrixXd A(motions.size(), 2);
Eigen::VectorXd x(motions.size());
for (size_t i = 0; i < motions.size(); ++i) {
const SE2& odomMotion = motions[i].odomMotion;
const SE2& laserMotion = motions[i].laserMotion;
const double& timeInterval = motions[i].timeInterval;
MotionMeasurement mm(odomMotion.translation().x(), odomMotion.translation().y(), odomMotion.rotation().angle(), timeInterval);
VelocityMeasurement velMeas = OdomConvert::convertToVelocity(mm);
A(i, 0) = velMeas.vl() * timeInterval;
A(i, 1) = velMeas.vr() * timeInterval;
x(i) = laserMotion.rotation().angle();
}
//linearSolution = (A.transpose() * A).inverse() * A.transpose() * x;
linearSolution = A.colPivHouseholderQr().solve(x);
//cout << PVAR(linearSolution.transpose()) << endl;
}
//constructing non-linear least squares
VertexSE2* laserOffset = new VertexSE2;
laserOffset->setId(Gm2dlIO::ID_LASERPOSE);
laserOffset->setEstimate(initialLaserPose);
optimizer.addVertex(laserOffset);
VertexBaseline* odomParamsVertex = new VertexBaseline;
odomParamsVertex->setId(Gm2dlIO::ID_ODOMCALIB);
odomParamsVertex->setEstimate(1.);
optimizer.addVertex(odomParamsVertex);
for (size_t i = 0; i < motions.size(); ++i) {
const SE2& odomMotion = motions[i].odomMotion;
const SE2& laserMotion = motions[i].laserMotion;
const double& timeInterval = motions[i].timeInterval;
// add the edge
MotionMeasurement mm(odomMotion.translation().x(), odomMotion.translation().y(), odomMotion.rotation().angle(), timeInterval);
OdomAndLaserMotion meas;
meas.velocityMeasurement = OdomConvert::convertToVelocity(mm);
meas.laserMotion = laserMotion;
EdgeCalib* calibEdge = new EdgeCalib;
calibEdge->setVertex(0, laserOffset);
calibEdge->setVertex(1, odomParamsVertex);
calibEdge->setInformation(Eigen::Matrix3d::Identity());
calibEdge->setMeasurement(meas);
if (! optimizer.addEdge(calibEdge)) {
cerr << "Error adding calib edge" << endl;
delete calibEdge;
}
}
if (fixLaser) {
cerr << "Fix position of the laser offset" << endl;
laserOffset->setFixed(true);
}
cerr << "\nPerforming partial non-linear estimation" << endl;
optimizer.initializeOptimization();
optimizer.computeActiveErrors();
optimizer.optimize(maxIterations);
cerr << "Calibrated laser offset (x, y, theta):" << laserOffset->estimate().toVector().transpose() << endl;
odomCalib(0) = -1. * linearSolution(0) * odomParamsVertex->estimate();
odomCalib(1) = linearSolution(1) * odomParamsVertex->estimate();
odomCalib(2) = odomParamsVertex->estimate();
cerr << "Odometry parameters (scaling factors (v_l, v_r, b)): " << odomCalib.transpose() << endl;
{
SE2 closedFormLaser;
Eigen::Vector3d closedFormOdom;
ClosedFormCalibration::calibrate(motions, closedFormLaser, closedFormOdom);
cerr << "\nObtaining closed form solution" << endl;
cerr << "Calibrated laser offset (x, y, theta):" << closedFormLaser.toVector().transpose() << endl;
cerr << "Odometry parameters (scaling factors (v_l, v_r, b)): " << closedFormOdom.transpose() << endl;
}
if (dumpGraphFilename.size() > 0) {
cerr << "Writing " << dumpGraphFilename << " ... ";
optimizer.save(dumpGraphFilename.c_str());
cerr << "done." << endl;
}
// optional input of a separate file for applying the odometry calibration
if (odomTestFilename.size() > 0) {
DataQueue testRobotLaserQueue;
int numTestOdom = Gm2dlIO::readRobotLaser(odomTestFilename, testRobotLaserQueue);
if (numTestOdom == 0) {
cerr << "Unable to read test data" << endl;
} else {
ofstream rawStream("odometry_raw.txt");
ofstream calibratedStream("odometry_calibrated.txt");
RobotLaser* prev = dynamic_cast<RobotLaser*>(testRobotLaserQueue.buffer().begin()->second);
SE2 prevCalibratedPose = prev->odomPose();
for (DataQueue::Buffer::const_iterator it = testRobotLaserQueue.buffer().begin(); it != testRobotLaserQueue.buffer().end(); ++it) {
RobotLaser* cur = dynamic_cast<RobotLaser*>(it->second);
assert(cur);
double dt = cur->timestamp() - prev->timestamp();
SE2 motion = prev->odomPose().inverse() * cur->odomPose();
// convert to velocity measurement
MotionMeasurement motionMeasurement(motion.translation().x(), motion.translation().y(), motion.rotation().angle(), dt);
VelocityMeasurement velocityMeasurement = OdomConvert::convertToVelocity(motionMeasurement);
// apply calibration
VelocityMeasurement calibratedVelocityMeasurment = velocityMeasurement;
calibratedVelocityMeasurment.setVl(odomCalib(0) * calibratedVelocityMeasurment.vl());
calibratedVelocityMeasurment.setVr(odomCalib(1) * calibratedVelocityMeasurment.vr());
MotionMeasurement mm = OdomConvert::convertToMotion(calibratedVelocityMeasurment, odomCalib(2));
// combine calibrated odometry with the previous pose
SE2 remappedOdom;
remappedOdom.fromVector(mm.measurement());
SE2 calOdomPose = prevCalibratedPose * remappedOdom;
// write output
rawStream << prev->odomPose().translation().x() << " " << prev->odomPose().translation().y() << " " << prev->odomPose().rotation().angle() << endl;
calibratedStream << calOdomPose.translation().x() << " " << calOdomPose.translation().y() << " " << calOdomPose.rotation().angle() << endl;
prevCalibratedPose = calOdomPose;
prev = cur;
}
}
}
return 0;
}
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;
}
int main(int argc, char **argv)
{
int num_points = 0;
// check for arg, # of points to use in projection SBA
if (argc > 1)
num_points = atoi(argv[1]);
double euc_noise = 0.1; // noise in position, m
double pix_noise = 1.0; // pixel noise
// double outlier_ratio = 0.1;
SparseOptimizer optimizer;
optimizer.setVerbose(false);
// variable-size block solver
BlockSolverX::LinearSolverType * linearSolver
= new LinearSolverCSparse<g2o
::BlockSolverX::PoseMatrixType>();
BlockSolverX * solver_ptr
= new BlockSolverX(linearSolver);
g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
optimizer.setAlgorithm(solver);
vector<Vector3d> true_points;
for (size_t i=0;i<1000; ++i)
{
true_points.push_back(Vector3d((Sample::uniform()-0.5)*3,
Sample::uniform()-0.5,
Sample::uniform()+10));
}
// set up camera params
Vector2d focal_length(500,500); // pixels
Vector2d principal_point(320,240); // 640x480 image
double baseline = 0.075; // 7.5 cm baseline
// set up camera params and projection matrices on vertices
g2o::VertexSCam::setKcam(focal_length[0],focal_length[1],
principal_point[0],principal_point[1],
baseline);
// set up two poses
int vertex_id = 0;
for (size_t i=0; i<2; ++i)
{
// set up rotation and translation for this node
Vector3d t(0,0,i);
Quaterniond q;
q.setIdentity();
Eigen::Isometry3d cam; // camera pose
cam = q;
cam.translation() = t;
// set up node
VertexSCam *vc = new VertexSCam();
vc->setEstimate(cam);
vc->setId(vertex_id); // vertex id
cerr << t.transpose() << " | " << q.coeffs().transpose() << endl;
// set first cam pose fixed
if (i==0)
vc->setFixed(true);
// make sure projection matrices are set
vc->setAll();
// add to optimizer
optimizer.addVertex(vc);
vertex_id++;
}
// set up point matches for GICP
for (size_t i=0; i<true_points.size(); ++i)
{
// get two poses
VertexSE3* vp0 =
dynamic_cast<VertexSE3*>(optimizer.vertices().find(0)->second);
VertexSE3* vp1 =
dynamic_cast<VertexSE3*>(optimizer.vertices().find(1)->second);
// calculate the relative 3D position of the point
Vector3d pt0,pt1;
pt0 = vp0->estimate().inverse() * true_points[i];
pt1 = vp1->estimate().inverse() * true_points[i];
// add in noise
pt0 += Vector3d(Sample::gaussian(euc_noise ),
Sample::gaussian(euc_noise ),
Sample::gaussian(euc_noise ));
pt1 += Vector3d(Sample::gaussian(euc_noise ),
Sample::gaussian(euc_noise ),
Sample::gaussian(euc_noise ));
// form edge, with normals in varioius positions
Vector3d nm0, nm1;
nm0 << 0, i, 1;
nm1 << 0, i, 1;
nm0.normalize();
nm1.normalize();
Edge_V_V_GICP * e // new edge with correct cohort for caching
= new Edge_V_V_GICP();
e->vertices()[0] // first viewpoint
= dynamic_cast<OptimizableGraph::Vertex*>(vp0);
e->vertices()[1] // second viewpoint
= dynamic_cast<OptimizableGraph::Vertex*>(vp1);
EdgeGICP meas;
meas.pos0 = pt0;
meas.pos1 = pt1;
meas.normal0 = nm0;
meas.normal1 = nm1;
e->setMeasurement(meas);
meas = e->measurement();
// e->inverseMeasurement().pos() = -kp;
// use this for point-plane
e->information() = meas.prec0(0.01);
// use this for point-point
// e->information().setIdentity();
// e->setRobustKernel(true);
//e->setHuberWidth(0.01);
optimizer.addEdge(e);
}
// set up SBA projections with some number of points
true_points.clear();
for (int i=0;i<num_points; ++i)
{
true_points.push_back(Vector3d((Sample::uniform()-0.5)*3,
Sample::uniform()-0.5,
Sample::uniform()+10));
}
// add point projections to this vertex
for (size_t i=0; i<true_points.size(); ++i)
{
g2o::VertexSBAPointXYZ * v_p
= new g2o::VertexSBAPointXYZ();
v_p->setId(vertex_id++);
v_p->setMarginalized(true);
v_p->setEstimate(true_points.at(i)
+ Vector3d(Sample::gaussian(1),
Sample::gaussian(1),
Sample::gaussian(1)));
optimizer.addVertex(v_p);
for (size_t j=0; j<2; ++j)
{
Vector3d z;
dynamic_cast<g2o::VertexSCam*>
(optimizer.vertices().find(j)->second)
->mapPoint(z,true_points.at(i));
if (z[0]>=0 && z[1]>=0 && z[0]<640 && z[1]<480)
{
z += Vector3d(Sample::gaussian(pix_noise),
Sample::gaussian(pix_noise),
Sample::gaussian(pix_noise/16.0));
g2o::Edge_XYZ_VSC * e
= new g2o::Edge_XYZ_VSC();
e->vertices()[0]
= dynamic_cast<g2o::OptimizableGraph::Vertex*>(v_p);
e->vertices()[1]
= dynamic_cast<g2o::OptimizableGraph::Vertex*>
(optimizer.vertices().find(j)->second);
e->setMeasurement(z);
//e->inverseMeasurement() = -z;
e->information() = Matrix3d::Identity();
//e->setRobustKernel(false);
//e->setHuberWidth(1);
optimizer.addEdge(e);
}
}
} // done with adding projection points
// move second cam off of its true position
VertexSE3* vc =
dynamic_cast<VertexSE3*>(optimizer.vertices().find(1)->second);
Eigen::Isometry3d cam = vc->estimate();
cam.translation() = Vector3d(-0.1,0.1,0.2);
vc->setEstimate(cam);
optimizer.initializeOptimization();
optimizer.computeActiveErrors();
cout << "Initial chi2 = " << FIXED(optimizer.chi2()) << endl;
optimizer.setVerbose(true);
optimizer.optimize(20);
cout << endl << "Second vertex should be near 0,0,1" << endl;
cout << dynamic_cast<VertexSE3*>(optimizer.vertices().find(0)->second)
->estimate().translation().transpose() << endl;
cout << dynamic_cast<VertexSE3*>(optimizer.vertices().find(1)->second)
->estimate().translation().transpose() << endl;
}
void Optimizer::optimizeUseG2O()
{
// create the linear solver
BlockSolverX::LinearSolverType * linearSolver = new LinearSolverCSparse<BlockSolverX::PoseMatrixType>();
// create the block solver on top of the linear solver
BlockSolverX* blockSolver = new BlockSolverX(linearSolver);
// create the algorithm to carry out the optimization
//OptimizationAlgorithmGaussNewton* optimizationAlgorithm = new OptimizationAlgorithmGaussNewton(blockSolver);
OptimizationAlgorithmLevenberg* optimizationAlgorithm = new OptimizationAlgorithmLevenberg(blockSolver);
// NOTE: We skip to fix a variable here, either this is stored in the file
// itself or Levenberg will handle it.
// create the optimizer to load the data and carry out the optimization
SparseOptimizer optimizer;
SparseOptimizer::initMultiThreading();
optimizer.setVerbose(true);
optimizer.setAlgorithm(optimizationAlgorithm);
{
pcl::ScopeTime time("G2O setup Graph vertices");
for (size_t cloud_count = 0; cloud_count < m_pointClouds.size(); ++cloud_count)
{
VertexSE3 *vertex = new VertexSE3;
vertex->setId(cloud_count);
Isometry3D affine = Isometry3D::Identity();
affine.linear() = m_pointClouds[cloud_count]->sensor_orientation_.toRotationMatrix().cast<Isometry3D::Scalar>();
affine.translation() = m_pointClouds[cloud_count]->sensor_origin_.block<3, 1>(0, 0).cast<Isometry3D::Scalar>();
vertex->setEstimate(affine);
optimizer.addVertex(vertex);
}
optimizer.vertex(0)->setFixed(true);
}
{
pcl::ScopeTime time("G2O setup Graph edges");
double trans_noise = 0.5, rot_noise = 0.5235;
EdgeSE3::InformationType infomation = EdgeSE3::InformationType::Zero();
infomation.block<3, 3>(0, 0) << trans_noise * trans_noise, 0, 0,
0, trans_noise * trans_noise, 0,
0, 0, trans_noise * trans_noise;
infomation.block<3, 3>(3, 3) << rot_noise * rot_noise, 0, 0,
0, rot_noise * rot_noise, 0,
0, 0, rot_noise * rot_noise;
for (size_t pair_count = 0; pair_count < m_cloudPairs.size(); ++pair_count)
{
CloudPair pair = m_cloudPairs[pair_count];
int from = pair.corresIdx.first;
int to = pair.corresIdx.second;
EdgeSE3 *edge = new EdgeSE3;
edge->vertices()[0] = optimizer.vertex(from);
edge->vertices()[1] = optimizer.vertex(to);
Eigen::Matrix<double, 6, 6> ATA = Eigen::Matrix<double, 6, 6>::Zero();
Eigen::Matrix<double, 6, 1> ATb = Eigen::Matrix<double, 6, 1>::Zero();
#pragma unroll 8
for (size_t point_count = 0; point_count < pair.corresPointIdx.size(); ++point_count) {
int point_p = pair.corresPointIdx[point_count].first;
int point_q = pair.corresPointIdx[point_count].second;
PointType P = m_pointClouds[from]->points[point_p];
PointType Q = m_pointClouds[to]->points[point_q];
Eigen::Vector3d p = P.getVector3fMap().cast<double>();
Eigen::Vector3d q = Q.getVector3fMap().cast<double>();
Eigen::Vector3d Np = P.getNormalVector3fMap().cast<double>();
double b = (p - q).dot(Np);
Eigen::Matrix<double, 6, 1> A_p;
A_p.block<3, 1>(0, 0) = p.cross(Np);
A_p.block<3, 1>(3, 0) = Np;
ATA += A_p * A_p.transpose();
ATb += A_p * b;
}
Eigen::Matrix<double, 6, 1> X = ATA.ldlt().solve(ATb);
Isometry3D measure = Isometry3D::Identity();
float beta = X[0];
float gammar = X[1];
float alpha = X[2];
measure.linear() = (Eigen::Matrix3d)Eigen::AngleAxisd(alpha, Eigen::Vector3d::UnitZ()) *
Eigen::AngleAxisd(gammar, Eigen::Vector3d::UnitY()) *
Eigen::AngleAxisd(beta, Eigen::Vector3d::UnitX());
measure.translation() = X.block<3, 1>(3, 0);
edge->setMeasurement(measure);
edge->setInformation(infomation);
optimizer.addEdge(edge);
}
}
optimizer.save("debug_preOpt.g2o");
{
pcl::ScopeTime time("g2o optimizing");
optimizer.initializeOptimization();
optimizer.optimize(30);
}
optimizer.save("debug_postOpt.g2o");
for (size_t cloud_count = 0; cloud_count < m_pointClouds.size(); ++cloud_count)
{
CloudTypePtr tmp(new CloudType);
Isometry3D trans = ((VertexSE3 *)optimizer.vertices()[cloud_count])->estimate();
Eigen::Affine3d affine;
affine.linear() = trans.rotation();
affine.translation() = trans.translation();
pcl::transformPointCloudWithNormals(*m_pointClouds[cloud_count], *tmp, affine.cast<float>());
pcl::copyPointCloud(*tmp, *m_pointClouds[cloud_count]);
}
PCL_WARN("Opitimization DONE!!!!\n");
if (m_params.saveDirectory.length()) {
if (boost::filesystem::exists(m_params.saveDirectory) && !boost::filesystem::is_directory(m_params.saveDirectory)) {
boost::filesystem::remove(m_params.saveDirectory);
}
boost::filesystem::create_directories(m_params.saveDirectory);
char filename[1024] = { 0 };
for (size_t i = 0; i < m_pointClouds.size(); ++i) {
sprintf(filename, "%s/cloud_%d.ply", m_params.saveDirectory.c_str(), i);
pcl::io::savePLYFileBinary(filename, *m_pointClouds[i]);
}
}
}