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added control over minimum rank in ShonanAveraging example, and resolved hard-coded sigma in FrobeniusFactor

release/4.3a0
lcarlone 2020-09-26 19:06:55 -04:00
parent 3734039bf5
commit 6567422ec5
2 changed files with 22 additions and 10 deletions
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24
examples/ShonanAveragingCLI.cpp
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@ -25,6 +25,8 @@
* Read 3D dataset sphere25000.txt and output to shonan.g2o (default) * Read 3D dataset sphere25000.txt and output to shonan.g2o (default)
* ./ShonanAveragingCLI -i spere2500.txt * ./ShonanAveragingCLI -i spere2500.txt
* *
* If you prefer using a robust Huber loss, you can add the option "-h true", for instance"
* ./ShonanAveragingCLI -i spere2500.txt -u true
*/ */
#include <gtsam/base/timing.h> #include <gtsam/base/timing.h>
@ -43,7 +45,8 @@ int main(int argc, char* argv[]) {
string datasetName; string datasetName;
string inputFile; string inputFile;
string outputFile; string outputFile;
int d, seed; int d, seed, pMin;
bool useHuberLoss;
po::options_description desc( po::options_description desc(
"Shonan Rotation Averaging CLI reads a *pose* graph, extracts the " "Shonan Rotation Averaging CLI reads a *pose* graph, extracts the "
"rotation constraints, and runs the Shonan algorithm."); "rotation constraints, and runs the Shonan algorithm.");
@ -58,6 +61,10 @@ int main(int argc, char* argv[]) {
"Write solution to the specified file")( "Write solution to the specified file")(
"dimension,d", po::value<int>(&d)->default_value(3), "dimension,d", po::value<int>(&d)->default_value(3),
"Optimize over 2D or 3D rotations")( "Optimize over 2D or 3D rotations")(
"useHuberLoss,h", po::value<bool>(&useHuberLoss)->default_value(false),
"set True to use Huber loss")(
"pMin,p", po::value<int>(&pMin)->default_value(3),
"set to use desired rank pMin")(
"seed,s", po::value<int>(&seed)->default_value(42), "seed,s", po::value<int>(&seed)->default_value(42),
"Random seed for initial estimate"); "Random seed for initial estimate");
po::variables_map vm; po::variables_map vm;
@ -85,11 +92,14 @@ int main(int argc, char* argv[]) {
NonlinearFactorGraph::shared_ptr inputGraph; NonlinearFactorGraph::shared_ptr inputGraph;
Values::shared_ptr posesInFile; Values::shared_ptr posesInFile;
Values poses; Values poses;
auto lmParams = LevenbergMarquardtParams::CeresDefaults();
if (d == 2) { if (d == 2) {
cout << "Running Shonan averaging for SO(2) on " << inputFile << endl; cout << "Running Shonan averaging for SO(2) on " << inputFile << endl;
ShonanAveraging2 shonan(inputFile); ShonanAveraging2::Parameters parameters(lmParams);
parameters.setUseHuber(useHuberLoss);
ShonanAveraging2 shonan(inputFile,parameters);
auto initial = shonan.initializeRandomly(rng); auto initial = shonan.initializeRandomly(rng);
auto result = shonan.run(initial); auto result = shonan.run(initial,pMin);
// Parse file again to set up translation problem, adding a prior // Parse file again to set up translation problem, adding a prior
boost::tie(inputGraph, posesInFile) = load2D(inputFile); boost::tie(inputGraph, posesInFile) = load2D(inputFile);
@ -101,9 +111,11 @@ int main(int argc, char* argv[]) {
poses = initialize::computePoses<Pose2>(result.first, &poseGraph); poses = initialize::computePoses<Pose2>(result.first, &poseGraph);
} else if (d == 3) { } else if (d == 3) {
cout << "Running Shonan averaging for SO(3) on " << inputFile << endl; cout << "Running Shonan averaging for SO(3) on " << inputFile << endl;
ShonanAveraging3 shonan(inputFile); ShonanAveraging3::Parameters parameters(lmParams);
parameters.setUseHuber(useHuberLoss);
ShonanAveraging3 shonan(inputFile,parameters);
auto initial = shonan.initializeRandomly(rng); auto initial = shonan.initializeRandomly(rng);
auto result = shonan.run(initial); auto result = shonan.run(initial,pMin);
// Parse file again to set up translation problem, adding a prior // Parse file again to set up translation problem, adding a prior
boost::tie(inputGraph, posesInFile) = load3D(inputFile); boost::tie(inputGraph, posesInFile) = load3D(inputFile);
@ -118,7 +130,7 @@ int main(int argc, char* argv[]) {
return 1; return 1;
} }
cout << "Writing result to " << outputFile << endl; cout << "Writing result to " << outputFile << endl;
writeG2o(NonlinearFactorGraph(), poses, outputFile); writeG2o(*inputGraph, poses, outputFile);
return 0; return 0;
} }

8
gtsam/slam/FrobeniusFactor.cpp
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@ -30,11 +30,11 @@ ConvertNoiseModel(const SharedNoiseModel &model, size_t d, bool defaultToUnit) {
const auto &robust = boost::dynamic_pointer_cast<noiseModel::Robust>(model); const auto &robust = boost::dynamic_pointer_cast<noiseModel::Robust>(model);
Vector sigmas; Vector sigmas;
if(robust){ if(robust){
sigma = 1; // Rot2 sigmas = robust->noise()->sigmas();
goto exit; } else{
} //else: sigmas = model->sigmas();
}
sigmas = model->sigmas();
size_t n = sigmas.size(); size_t n = sigmas.size();
if (n == 1) { if (n == 1) {
sigma = sigmas(0); // Rot2 sigma = sigmas(0); // Rot2