131 lines
4.5 KiB
C++
131 lines
4.5 KiB
C++
/**
|
|
* @file testGradientDescentOptimizer.cpp
|
|
* @brief
|
|
* @author ydjian
|
|
* @date Jun 11, 2012
|
|
*/
|
|
|
|
#include <gtsam/slam/pose2SLAM.h>
|
|
#include <gtsam/nonlinear/GradientDescentOptimizer.h>
|
|
|
|
#include <CppUnitLite/TestHarness.h>
|
|
|
|
#include <boost/make_shared.hpp>
|
|
#include <boost/shared_ptr.hpp>
|
|
#include <boost/tuple/tuple.hpp>
|
|
|
|
|
|
using namespace std;
|
|
using namespace gtsam;
|
|
|
|
|
|
boost::tuple<pose2SLAM::Graph, Values> generateProblem() {
|
|
|
|
// 1. Create graph container and add factors to it
|
|
pose2SLAM::Graph graph ;
|
|
|
|
// 2a. Add Gaussian prior
|
|
Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
|
|
SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
|
|
graph.addPrior(1, priorMean, priorNoise);
|
|
|
|
// 2b. Add odometry factors
|
|
SharedDiagonal odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
|
|
graph.addOdometry(1, 2, Pose2(2.0, 0.0, 0.0 ), odometryNoise);
|
|
graph.addOdometry(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
|
|
graph.addOdometry(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
|
|
graph.addOdometry(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
|
|
|
|
// 2c. Add pose constraint
|
|
SharedDiagonal constraintUncertainty = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
|
|
graph.addConstraint(5, 2, Pose2(2.0, 0.0, M_PI_2), constraintUncertainty);
|
|
|
|
// 3. Create the data structure to hold the initialEstimate estinmate to the solution
|
|
pose2SLAM::Values initialEstimate;
|
|
Pose2 x1(0.5, 0.0, 0.2 ); initialEstimate.insertPose(1, x1);
|
|
Pose2 x2(2.3, 0.1,-0.2 ); initialEstimate.insertPose(2, x2);
|
|
Pose2 x3(4.1, 0.1, M_PI_2); initialEstimate.insertPose(3, x3);
|
|
Pose2 x4(4.0, 2.0, M_PI ); initialEstimate.insertPose(4, x4);
|
|
Pose2 x5(2.1, 2.1,-M_PI_2); initialEstimate.insertPose(5, x5);
|
|
|
|
return boost::tie(graph, initialEstimate);
|
|
}
|
|
|
|
|
|
/* ************************************************************************* */
|
|
TEST(optimize, GradientDescentOptimizer) {
|
|
|
|
pose2SLAM::Graph graph ;
|
|
pose2SLAM::Values initialEstimate;
|
|
|
|
boost::tie(graph, initialEstimate) = generateProblem();
|
|
// cout << "initial error = " << graph.error(initialEstimate) << endl ;
|
|
|
|
// Single Step Optimization using Levenberg-Marquardt
|
|
NonlinearOptimizerParams param;
|
|
param.maxIterations = 500; /* requires a larger number of iterations to converge */
|
|
param.verbosity = NonlinearOptimizerParams::SILENT;
|
|
|
|
GradientDescentOptimizer optimizer(graph, initialEstimate, param);
|
|
Values result = optimizer.optimize();
|
|
// cout << "gd1 solver final error = " << graph.error(result) << endl;
|
|
|
|
/* the optimality of the solution is not comparable to the */
|
|
DOUBLES_EQUAL(0.0, graph.error(result), 1e-2);
|
|
|
|
CHECK(1);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(optimize, ConjugateGradientOptimizer) {
|
|
|
|
pose2SLAM::Graph graph ;
|
|
pose2SLAM::Values initialEstimate;
|
|
|
|
boost::tie(graph, initialEstimate) = generateProblem();
|
|
// cout << "initial error = " << graph.error(initialEstimate) << endl ;
|
|
|
|
// Single Step Optimization using Levenberg-Marquardt
|
|
NonlinearOptimizerParams param;
|
|
param.maxIterations = 500; /* requires a larger number of iterations to converge */
|
|
param.verbosity = NonlinearOptimizerParams::SILENT;
|
|
|
|
|
|
ConjugateGradientOptimizer optimizer(graph, initialEstimate, param, true);
|
|
Values result = optimizer.optimize();
|
|
// cout << "cg final error = " << graph.error(result) << endl;
|
|
|
|
/* the optimality of the solution is not comparable to the */
|
|
DOUBLES_EQUAL(0.0, graph.error(result), 1e-2);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(optimize, GradientDescentOptimizer2) {
|
|
|
|
pose2SLAM::Graph graph ;
|
|
pose2SLAM::Values initialEstimate;
|
|
|
|
boost::tie(graph, initialEstimate) = generateProblem();
|
|
// cout << "initial error = " << graph.error(initialEstimate) << endl ;
|
|
|
|
// Single Step Optimization using Levenberg-Marquardt
|
|
NonlinearOptimizerParams param;
|
|
param.maxIterations = 500; /* requires a larger number of iterations to converge */
|
|
param.verbosity = NonlinearOptimizerParams::SILENT;
|
|
|
|
|
|
ConjugateGradientOptimizer optimizer(graph, initialEstimate, param, false);
|
|
Values result = optimizer.optimize();
|
|
// cout << "gd2 solver final error = " << graph.error(result) << endl;
|
|
|
|
/* the optimality of the solution is not comparable to the */
|
|
DOUBLES_EQUAL(0.0, graph.error(result), 1e-2);
|
|
}
|
|
|
|
|
|
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
|
|
/* ************************************************************************* */
|