testGradientDescentOptimizer.cpp

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#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/geometry/Pose2.h>

#include <CppUnitLite/TestHarness.h>


using namespace std;
using namespace gtsam;

// Generate a small PoseSLAM problem
std::tuple<NonlinearFactorGraph, Values> generateProblem() {

  // 1. Create graph container and add factors to it
  NonlinearFactorGraph graph;

  // 2a. Add Gaussian prior
  Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
  SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas(
      Vector3(0.3, 0.3, 0.1));
  graph.addPrior(1, priorMean, priorNoise);

  // 2b. Add odometry factors
  SharedDiagonal odometryNoise = noiseModel::Diagonal::Sigmas(
      Vector3(0.2, 0.2, 0.1));
  graph.emplace_shared<BetweenFactor<Pose2>>(1, 2, Pose2(2.0, 0.0, 0.0), odometryNoise);
  graph.emplace_shared<BetweenFactor<Pose2>>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
  graph.emplace_shared<BetweenFactor<Pose2>>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
  graph.emplace_shared<BetweenFactor<Pose2>>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise);

  // 2c. Add pose constraint
  SharedDiagonal constraintUncertainty = noiseModel::Diagonal::Sigmas(
      Vector3(0.2, 0.2, 0.1));
  graph.emplace_shared<BetweenFactor<Pose2>>(5, 2, Pose2(2.0, 0.0, M_PI_2),
      constraintUncertainty);

  // 3. Create the data structure to hold the initialEstimate estimate to the solution
  Values initialEstimate;
  Pose2 x1(0.5, 0.0, 0.2);
  initialEstimate.insert(1, x1);
  Pose2 x2(2.3, 0.1, -0.2);
  initialEstimate.insert(2, x2);
  Pose2 x3(4.1, 0.1, M_PI_2);
  initialEstimate.insert(3, x3);
  Pose2 x4(4.0, 2.0, M_PI);
  initialEstimate.insert(4, x4);
  Pose2 x5(2.1, 2.1, -M_PI_2);
  initialEstimate.insert(5, x5);

  return {graph, initialEstimate};
}

/* ************************************************************************* */
TEST(NonlinearConjugateGradientOptimizer, Optimize) {
const auto [graph, initialEstimate] = generateProblem();
//  cout << "initial error = " << graph.error(initialEstimate) << endl;

  NonlinearOptimizerParams param;
  param.maxIterations = 500; /* requires a larger number of iterations to converge */
  param.verbosity = NonlinearOptimizerParams::SILENT;

  NonlinearConjugateGradientOptimizer optimizer(graph, initialEstimate, param);
  Values result = optimizer.optimize();
//  cout << "cg final error = " << graph.error(result) << endl;

  EXPECT_DOUBLES_EQUAL(0.0, graph.error(result), 1e-4);
}

/* ************************************************************************* */
int main() {
  TestResult tr;
  return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */