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attempting robustification in Frobenius factor

release/4.3a0
lcarlone 2020-09-24 18:24:23 -04:00
parent 73600c8faa
commit 564e623f44
3 changed files with 57 additions and 14 deletions
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30
gtsam/sfm/ShonanAveraging.cpp
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@ -139,13 +139,21 @@ ShonanAveraging<d>::ShonanAveraging(const Measurements &measurements,
/* ************************************************************************* */
template <size_t d>
NonlinearFactorGraph ShonanAveraging<d>::buildGraphAt(size_t p) const {
std::cout << "zz0" << std::endl;
NonlinearFactorGraph graph;
auto G = boost::make_shared<Matrix>(SO<-1>::VectorizedGenerators(p));
std::cout << "zz1" << std::endl;
for (const auto &measurement : measurements_) {
const auto &keys = measurement.keys();
const auto &Rij = measurement.measured();
const auto &model = measurement.noiseModel();
measurement.print("measurement");
std::cout << "zzzz1" << std::endl;
model->print();
std::cout << "zzzz2" << std::endl;
graph.emplace_shared<ShonanFactor<d>>(keys[0], keys[1], Rij, p, model, G);
std::cout << "zzzz3" << std::endl;
}
// Possibly add Karcher prior
@ -153,13 +161,13 @@ NonlinearFactorGraph ShonanAveraging<d>::buildGraphAt(size_t p) const {
const size_t dim = SOn::Dimension(p);
graph.emplace_shared<KarcherMeanFactor<SOn>>(graph.keys(), dim);
}
std::cout << "zz2" << std::endl;
// Possibly add gauge factors - they are probably useless as gradient is zero
if (parameters_.gamma > 0 && p > d + 1) {
for (auto key : graph.keys())
graph.emplace_shared<ShonanGaugeFactor>(key, p, d, parameters_.gamma);
}
std::cout << "z3" << std::endl;
return graph;
}
@ -177,6 +185,7 @@ ShonanAveraging<d>::createOptimizerAt(size_t p, const Values &initial) const {
// Build graph
NonlinearFactorGraph graph = buildGraphAt(p);
std::cout << "yy1" << std::endl;
// Anchor prior is added here as depends on initial value (and cost is zero)
if (parameters_.alpha > 0) {
size_t i;
@ -187,7 +196,7 @@ ShonanAveraging<d>::createOptimizerAt(size_t p, const Values &initial) const {
graph.emplace_shared<PriorFactor<SOn>>(i, SOn::Lift(p, value.matrix()),
model);
}
std::cout << "yy2" << std::endl;
// Optimize
return boost::make_shared<LevenbergMarquardtOptimizer>(graph, initial,
parameters_.lm);
@ -197,7 +206,9 @@ ShonanAveraging<d>::createOptimizerAt(size_t p, const Values &initial) const {
template <size_t d>
Values ShonanAveraging<d>::tryOptimizingAt(size_t p,
const Values &initial) const {
std::cout << "xx1" << std::endl;
auto lm = createOptimizerAt(p, initial);
std::cout << "xx2" << std::endl;
return lm->optimize();
}
@ -803,17 +814,29 @@ std::pair<Values, double> ShonanAveraging<d>::run(const Values &initialEstimate,
Values initialSOp = LiftTo<Rot>(pMin, initialEstimate); // lift to pMin!
for (size_t p = pMin; p <= pMax; p++) {
// Optimize until convergence at this level
std::cout << "4a" << std::endl;
Qstar = tryOptimizingAt(p, initialSOp);
std::cout << "4aa" << std::endl;
if(parameters_.useHuber){ // in this case, there is no optimality verification
std::cout << "4aaa" << std::endl;
if(pMin!=pMax)
std::cout << "When using robust norm, Shonan only tests a single rank" << std::endl;
const Values SO3Values = roundSolution(Qstar);
return std::make_pair(SO3Values, 0);
}
std::cout << "4b" << std::endl;
// Check certificate of global optimzality
Vector minEigenVector;
double minEigenValue = computeMinEigenValue(Qstar, &minEigenVector);
std::cout << "4bb" << std::endl;
if (minEigenValue > parameters_.optimalityThreshold) {
// If at global optimum, round and return solution
const Values SO3Values = roundSolution(Qstar);
return std::make_pair(SO3Values, minEigenValue);
}
std::cout << "4c" << std::endl;
// Not at global optimimum yet, so check whether we will go to next level
if (p != pMax) {
// Calculate initial estimate for next level by following minEigenVector
@ -821,6 +844,7 @@ std::pair<Values, double> ShonanAveraging<d>::run(const Values &initialEstimate,
initializeWithDescent(p + 1, Qstar, minEigenVector, minEigenValue);
}
}
std::cout << "4d" << std::endl;
throw std::runtime_error("Shonan::run did not converge for given pMax");
}

16
gtsam/sfm/tests/testShonanAveraging.cpp
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@ -330,17 +330,23 @@ TEST(ShonanAveraging2, noisyToyGraphWithHuber) {
auto measurements = parseMeasurements<Rot2>(g2oFile);
parameters.setUseHuber(true);
parameters.print();
std::cout << "1" << std::endl;
ShonanAveraging2 shonan(measurements, parameters);
EXPECT_LONGS_EQUAL(4, shonan.nrUnknowns());
// Check graph building
NonlinearFactorGraph graph = shonan.buildGraphAt(2);
graph.print();
EXPECT_LONGS_EQUAL(6, graph.size());
std::cout << "2" << std::endl;
// NonlinearFactorGraph graph = shonan.buildGraphAt(2);
// graph.print();
// EXPECT_LONGS_EQUAL(6, graph.size());
std::cout << "3" << std::endl;
auto initial = shonan.initializeRandomly(kRandomNumberGenerator);
std::cout << "4" << std::endl;
auto result = shonan.run(initial, 2,3);
EXPECT_DOUBLES_EQUAL(0.0008211, shonan.cost(result.first), 1e-6);
EXPECT_DOUBLES_EQUAL(0, result.second, 1e-10); // certificate!
std::cout << "5" << std::endl;
// EXPECT_DOUBLES_EQUAL(0.0008211, shonan.cost(result.first), 1e-6);
// EXPECT_DOUBLES_EQUAL(0, result.second, 1e-10); // certificate!
}
/* ************************************************************************* */

17
gtsam/slam/FrobeniusFactor.cpp
View File

@ -26,8 +26,15 @@ namespace gtsam {
boost::shared_ptr<noiseModel::Isotropic>
ConvertNoiseModel(const SharedNoiseModel &model, size_t d, bool defaultToUnit) {
double sigma = 1.0;
std::cout << "111111" << std::endl;
if (model != nullptr) {
auto sigmas = model->sigmas();
const auto &robust = boost::dynamic_pointer_cast<noiseModel::Robust>(model);
Vector sigmas;
if(robust)
sigmas[0] = 1;
else
sigmas = model->sigmas();
size_t n = sigmas.size();
if (n == 1) {
sigma = sigmas(0); // Rot2
@ -47,7 +54,13 @@ ConvertNoiseModel(const SharedNoiseModel &model, size_t d, bool defaultToUnit) {
}
}
exit:
return noiseModel::Isotropic::Sigma(d, sigma);
auto isoModel = noiseModel::Isotropic::Sigma(d, sigma);
const auto &robust = boost::dynamic_pointer_cast<noiseModel::Robust>(model);
if(robust)
return noiseModel::Robust::Create(
noiseModel::mEstimator::Huber::Create(1.345), isoModel);
else
return isoModel;
}
//******************************************************************************