cleaned up code a bit
parent
b602e75a99
commit
f9db53fdb8
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@ -22,6 +22,7 @@
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#include <gtsam/base/FastSet.h>
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#include <boost/foreach.hpp>
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#include <boost/make_shared.hpp>
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using namespace std;
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@ -94,8 +95,7 @@ namespace gtsam {
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Index j = keys()[i];
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dkeys.push_back(DiscreteKey(j,cardinality(j)));
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}
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shared_ptr f(new DecisionTreeFactor(dkeys, result));
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return f;
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return boost::make_shared<DecisionTreeFactor>(dkeys, result);
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}
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/* ************************************************************************* */
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@ -15,13 +15,13 @@
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* @author Frank Dellaert
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*/
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#include <boost/make_shared.hpp>
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#include <gtsam/inference/SymbolicFactorGraph.h>
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#include <gtsam/inference/BayesNet.h>
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#include <gtsam/inference/EliminationTree.h>
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#include <gtsam/inference/IndexConditional.h>
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#include <boost/make_shared.hpp>
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namespace gtsam {
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using namespace std;
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@ -32,26 +32,22 @@ namespace gtsam {
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/* ************************************************************************* */
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void SymbolicFactorGraph::push_factor(Index key) {
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boost::shared_ptr<IndexFactor> factor(new IndexFactor(key));
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push_back(factor);
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push_back(boost::make_shared<IndexFactor>(key));
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}
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/** Push back binary factor */
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void SymbolicFactorGraph::push_factor(Index key1, Index key2) {
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boost::shared_ptr<IndexFactor> factor(new IndexFactor(key1,key2));
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push_back(factor);
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push_back(boost::make_shared<IndexFactor>(key1,key2));
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}
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/** Push back ternary factor */
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void SymbolicFactorGraph::push_factor(Index key1, Index key2, Index key3) {
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boost::shared_ptr<IndexFactor> factor(new IndexFactor(key1,key2,key3));
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push_back(factor);
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push_back(boost::make_shared<IndexFactor>(key1,key2,key3));
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}
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/** Push back 4-way factor */
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void SymbolicFactorGraph::push_factor(Index key1, Index key2, Index key3, Index key4) {
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boost::shared_ptr<IndexFactor> factor(new IndexFactor(key1,key2,key3,key4));
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push_back(factor);
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push_back(boost::make_shared<IndexFactor>(key1,key2,key3,key4));
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}
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/* ************************************************************************* */
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@ -66,7 +62,7 @@ namespace gtsam {
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/* ************************************************************************* */
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IndexFactor::shared_ptr CombineSymbolic(
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const FactorGraph<IndexFactor>& factors, const FastMap<Index,
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std::vector<Index> >& variableSlots) {
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vector<Index> >& variableSlots) {
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IndexFactor::shared_ptr combined(Combine<IndexFactor, Index> (factors, variableSlots));
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// combined->assertInvariants();
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return combined;
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@ -84,12 +80,9 @@ namespace gtsam {
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if (keys.size() < 1) throw invalid_argument(
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"IndexFactor::CombineAndEliminate called on factors with no variables.");
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pair<IndexConditional::shared_ptr, IndexFactor::shared_ptr> result;
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std::vector<Index> newKeys(keys.begin(),keys.end());
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result.first.reset(new IndexConditional(newKeys, nrFrontals));
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result.second.reset(new IndexFactor(newKeys.begin()+nrFrontals, newKeys.end()));
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return result;
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vector<Index> newKeys(keys.begin(), keys.end());
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return make_pair(new IndexConditional(newKeys, nrFrontals),
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new IndexFactor(newKeys.begin() + nrFrontals, newKeys.end()));
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}
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/* ************************************************************************* */
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@ -15,19 +15,20 @@
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* @author Frank Dellaert
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*/
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#include <stdarg.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/JacobianFactorGraph.h>
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#include <gtsam/linear/VectorValues.h>
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#include <gtsam/inference/BayesNet-inl.h>
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#include <boost/foreach.hpp>
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#include <boost/shared_ptr.hpp>
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#include <boost/tuple/tuple.hpp>
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#include <gtsam/linear/JacobianFactorGraph.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/VectorValues.h>
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#include <gtsam/inference/BayesNet-inl.h>
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#include <stdarg.h>
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using namespace std;
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using namespace gtsam;
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using boost::shared_ptr;
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// trick from some reading group
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#define FOREACH_PAIR( KEY, VAL, COL) BOOST_FOREACH (boost::tie(KEY,VAL),COL)
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@ -72,13 +73,13 @@ void push_front(GaussianBayesNet& bn, Index key, Vector d, Matrix R,
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}
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/* ************************************************************************* */
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boost::shared_ptr<VectorValues> allocateVectorValues(const GaussianBayesNet& bn) {
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shared_ptr<VectorValues> allocateVectorValues(const GaussianBayesNet& bn) {
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vector<size_t> dimensions(bn.size());
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Index var = 0;
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BOOST_FOREACH(const boost::shared_ptr<const GaussianConditional> conditional, bn) {
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BOOST_FOREACH(const shared_ptr<const GaussianConditional> conditional, bn) {
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dimensions[var++] = conditional->dim();
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}
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return boost::shared_ptr<VectorValues>(new VectorValues(dimensions));
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return shared_ptr<VectorValues>(new VectorValues(dimensions));
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}
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/* ************************************************************************* */
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@ -92,7 +93,7 @@ VectorValues optimize(const GaussianBayesNet& bn) {
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// (R*x)./sigmas = y by solving x=inv(R)*(y.*sigmas)
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void optimizeInPlace(const GaussianBayesNet& bn, VectorValues& x) {
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/** solve each node in turn in topological sort order (parents first)*/
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BOOST_REVERSE_FOREACH(const boost::shared_ptr<const GaussianConditional> cg, bn) {
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BOOST_REVERSE_FOREACH(const shared_ptr<const GaussianConditional> cg, bn) {
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// i^th part of R*x=y, x=inv(R)*y
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// (Rii*xi + R_i*x(i+1:))./si = yi <-> xi = inv(Rii)*(yi.*si - R_i*x(i+1:))
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cg->solveInPlace(x);
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@ -102,14 +103,14 @@ void optimizeInPlace(const GaussianBayesNet& bn, VectorValues& x) {
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/* ************************************************************************* */
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VectorValues backSubstitute(const GaussianBayesNet& bn, const VectorValues& input) {
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VectorValues output = input;
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BOOST_REVERSE_FOREACH(const boost::shared_ptr<const GaussianConditional> cg, bn) {
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BOOST_REVERSE_FOREACH(const shared_ptr<const GaussianConditional> cg, bn) {
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const Index key = *(cg->beginFrontals());
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Vector xS = internal::extractVectorValuesSlices(output, cg->beginParents(), cg->endParents());
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xS = input[key] - cg->get_S() * xS;
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output[key] = cg->get_R().triangularView<Eigen::Upper>().solve(xS);
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}
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BOOST_FOREACH(const boost::shared_ptr<const GaussianConditional> cg, bn) {
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BOOST_FOREACH(const shared_ptr<const GaussianConditional> cg, bn) {
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cg->scaleFrontalsBySigma(output);
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}
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@ -130,7 +131,7 @@ VectorValues backSubstituteTranspose(const GaussianBayesNet& bn,
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// we loop from first-eliminated to last-eliminated
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// i^th part of L*gy=gx is done block-column by block-column of L
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BOOST_FOREACH(const boost::shared_ptr<const GaussianConditional> cg, bn)
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BOOST_FOREACH(const shared_ptr<const GaussianConditional> cg, bn)
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cg->solveTransposeInPlace(gy);
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// Scale gy
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@ -196,7 +197,7 @@ pair<Matrix,Vector> matrix(const GaussianBayesNet& bn) {
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Index key; size_t I;
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FOREACH_PAIR(key,I,mapping) {
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// find corresponding conditional
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boost::shared_ptr<const GaussianConditional> cg = bn[key];
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shared_ptr<const GaussianConditional> cg = bn[key];
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// get sigmas
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Vector sigmas = cg->get_sigmas();
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@ -233,7 +234,7 @@ pair<Matrix,Vector> matrix(const GaussianBayesNet& bn) {
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double determinant(const GaussianBayesNet& bayesNet) {
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double logDet = 0.0;
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BOOST_FOREACH(boost::shared_ptr<const GaussianConditional> cg, bayesNet){
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BOOST_FOREACH(shared_ptr<const GaussianConditional> cg, bayesNet){
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logDet += cg->get_R().diagonal().unaryExpr(ptr_fun<double,double>(log)).sum();
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}
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@ -17,6 +17,9 @@
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#include <gtsam/slam/visualSLAM.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <boost/make_shared.hpp>
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using boost::make_shared;
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namespace visualSLAM {
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@ -62,49 +65,43 @@ namespace visualSLAM {
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/* ************************************************************************* */
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void Graph::addMeasurement(const Point2& measured, const SharedNoiseModel& model,
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Key poseKey, Key pointKey, const shared_ptrK K) {
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boost::shared_ptr<ProjectionFactor> factor(new ProjectionFactor(measured, model, poseKey, pointKey, K));
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push_back(factor);
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push_back(make_shared<ProjectionFactor>(measured, model, poseKey, pointKey, K));
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}
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/* ************************************************************************* */
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void Graph::addStereoMeasurement(const StereoPoint2& measured, const SharedNoiseModel& model,
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Key poseKey, Key pointKey, const shared_ptrKStereo K) {
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boost::shared_ptr<StereoFactor> factor(new StereoFactor(measured, model, poseKey, pointKey, K));
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push_back(factor);
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push_back(make_shared<StereoFactor>(measured, model, poseKey, pointKey, K));
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}
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/* ************************************************************************* */
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void Graph::addPoseConstraint(Key poseKey, const Pose3& p) {
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boost::shared_ptr<PoseConstraint> factor(new PoseConstraint(poseKey, p));
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push_back(factor);
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push_back(make_shared<PoseConstraint>(poseKey, p));
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}
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/* ************************************************************************* */
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void Graph::addPointConstraint(Key pointKey, const Point3& p) {
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boost::shared_ptr<PointConstraint> factor(new PointConstraint(pointKey, p));
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push_back(factor);
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push_back(make_shared<PointConstraint>(pointKey, p));
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}
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/* ************************************************************************* */
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void Graph::addPosePrior(Key poseKey, const Pose3& p, const SharedNoiseModel& model) {
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boost::shared_ptr<PosePrior> factor(new PosePrior(poseKey, p, model));
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push_back(factor);
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push_back(make_shared<PosePrior>(poseKey, p, model));
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}
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/* ************************************************************************* */
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void Graph::addPointPrior(Key pointKey, const Point3& p, const SharedNoiseModel& model) {
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boost::shared_ptr<PointPrior> factor(new PointPrior(pointKey, p, model));
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push_back(factor);
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push_back(make_shared<PointPrior>(pointKey, p, model));
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}
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/* ************************************************************************* */
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void Graph::addRangeFactor(Key poseKey, Key pointKey, double range, const SharedNoiseModel& model) {
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push_back(boost::shared_ptr<RangeFactor>(new RangeFactor(poseKey, pointKey, range, model)));
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push_back(make_shared<RangeFactor>(poseKey, pointKey, range, model));
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}
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/* ************************************************************************* */
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void Graph::addOdometry(Key x1, Key x2, const Pose3& odometry, const SharedNoiseModel& model) {
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push_back(boost::shared_ptr<BetweenFactor<Pose3> >(new BetweenFactor<Pose3>(x1, x2, odometry, model)));
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push_back(make_shared<BetweenFactor<Pose3> >(x1, x2, odometry, model));
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}
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/* ************************************************************************* */
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