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