87 lines
3.4 KiB
C++
87 lines
3.4 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file Pose2SLAMExampleExpressions.cpp
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* @brief Expressions version of Pose2SLAMExample.cpp
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* @date Oct 2, 2014
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* @author Frank Dellaert
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*/
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// The two new headers that allow using our Automatic Differentiation Expression framework
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#include <gtsam/slam/expressions.h>
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#include <gtsam/nonlinear/ExpressionFactorGraph.h>
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// For an explanation of headers below, please see Pose2SLAMExample.cpp
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
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#include <gtsam/nonlinear/Marginals.h>
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using namespace std;
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using namespace gtsam;
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int main(int argc, char** argv) {
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// 1. Create a factor graph container and add factors to it
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ExpressionFactorGraph graph;
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// Create Expressions for unknowns
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Pose2_ x1(1), x2(2), x3(3), x4(4), x5(5);
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// 2a. Add a prior on the first pose, setting it to the origin
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
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graph.addExpressionFactor(x1, Pose2(0, 0, 0), priorNoise);
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// For simplicity, we will use the same noise model for odometry and loop closures
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noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
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// 2b. Add odometry factors
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graph.addExpressionFactor(between(x1,x2), Pose2(2, 0, 0 ), model);
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graph.addExpressionFactor(between(x2,x3), Pose2(2, 0, M_PI_2), model);
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graph.addExpressionFactor(between(x3,x4), Pose2(2, 0, M_PI_2), model);
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graph.addExpressionFactor(between(x4,x5), Pose2(2, 0, M_PI_2), model);
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// 2c. Add the loop closure constraint
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graph.addExpressionFactor(between(x5,x2), Pose2(2, 0, M_PI_2), model);
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graph.print("\nFactor Graph:\n"); // print
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// 3. Create the data structure to hold the initialEstimate estimate to the solution
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// For illustrative purposes, these have been deliberately set to incorrect values
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Values initialEstimate;
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initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2 ));
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initialEstimate.insert(2, Pose2(2.3, 0.1, -0.2 ));
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initialEstimate.insert(3, Pose2(4.1, 0.1, M_PI_2));
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initialEstimate.insert(4, Pose2(4.0, 2.0, M_PI ));
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initialEstimate.insert(5, Pose2(2.1, 2.1, -M_PI_2));
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initialEstimate.print("\nInitial Estimate:\n"); // print
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// 4. Optimize the initial values using a Gauss-Newton nonlinear optimizer
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GaussNewtonParams parameters;
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parameters.relativeErrorTol = 1e-5;
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parameters.maxIterations = 100;
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GaussNewtonOptimizer optimizer(graph, initialEstimate, parameters);
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Values result = optimizer.optimize();
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result.print("Final Result:\n");
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// 5. Calculate and print marginal covariances for all variables
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cout.precision(3);
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Marginals marginals(graph, result);
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cout << "x1 covariance:\n" << marginals.marginalCovariance(1) << endl;
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cout << "x2 covariance:\n" << marginals.marginalCovariance(2) << endl;
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cout << "x3 covariance:\n" << marginals.marginalCovariance(3) << endl;
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cout << "x4 covariance:\n" << marginals.marginalCovariance(4) << endl;
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cout << "x5 covariance:\n" << marginals.marginalCovariance(5) << endl;
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return 0;
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}
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