27 using namespace gtsam;
29 int main(
const int argc,
const char* argv[]) {
40 boost::tie(graph, initial) =
readG2o(g2oFile, is3D);
43 auto priorModel = noiseModel::Diagonal::Variances(
44 (
Vector(6) << 1
e-6, 1
e-6, 1
e-6, 1
e-4, 1
e-4, 1
e-4).finished());
46 for (
const auto key_value : *initial) {
47 std::cout <<
"Adding prior to g2o file " << std::endl;
48 firstKey = key_value.key;
53 std::cout <<
"Optimizing the factor graph" << std::endl;
58 std::cout <<
"Optimization complete" << std::endl;
60 std::cout <<
"initial error=" << graph->error(*initial) << std::endl;
61 std::cout <<
"final error=" << graph->error(result) << std::endl;
64 result.
print(
"result");
67 std::cout <<
"Writing results to file: " << outputFile << std::endl;
70 boost::tie(graphNoKernel, initial2) =
readG2o(g2oFile);
71 writeG2o(*graphNoKernel, result, outputFile);
72 std::cout <<
"done! " << std::endl;
77 for (
const auto key_value : result) {
void writeG2o(const NonlinearFactorGraph &graph, const Values &estimate, const string &filename)
This function writes a g2o file from NonlinearFactorGraph and a Values structure. ...
virtual const Values & optimize()
NonlinearFactorGraph graph
string findExampleDataFile(const string &name)
boost::shared_ptr< This > shared_ptr
void setVerbosity(const std::string &src)
Array< double, 1, 3 > e(1./3., 0.5, 2.)
static SmartStereoProjectionParams params
void print(const std::string &str="", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const
Matrix marginalCovariance(Key variable) const
A class for computing marginals in a NonlinearFactorGraph.
int main(const int argc, const char *argv[])
GraphAndValues readG2o(const string &g2oFile, const bool is3D, KernelFunctionType kernelFunctionType)
This function parses a g2o file and stores the measurements into a NonlinearFactorGraph and the initi...
Eigen::Matrix< double, Eigen::Dynamic, 1 > Vector
utility functions for loading datasets
std::uint64_t Key
Integer nonlinear key type.
Marginals marginals(graph, result)