69 lines
2.5 KiB
C++
69 lines
2.5 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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#include <gtsam/linear/IterativeOptimizationParameters.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/slam/pose2SLAM.h>
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#include <boost/shared_ptr.hpp>
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#include <boost/make_shared.hpp>
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using namespace std;
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using namespace gtsam;
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/* ************************************************************************* */
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int main(void) {
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// 1. Create graph container and add factors to it
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pose2SLAM::Graph 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(Vector_(3, 0.3, 0.3, 0.1));
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graph.addPrior(1, priorMean, priorNoise);
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// 2b. Add odometry factors
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SharedDiagonal odometryNoise(Vector_(3, 0.2, 0.2, 0.1));
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graph.addOdometry(1, 2, Pose2(2.0, 0.0, 0.0 ), odometryNoise);
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graph.addOdometry(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
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graph.addOdometry(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
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graph.addOdometry(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(Vector_(3, 0.2, 0.2, 0.1));
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graph.addConstraint(5, 2, Pose2(2.0, 0.0, M_PI_2), constraintUncertainty);
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// print
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graph.print("\nFactor graph:\n");
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// 3. Create the data structure to hold the initialEstimate estinmate to the solution
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pose2SLAM::Values initialEstimate;
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Pose2 x1(0.5, 0.0, 0.2 ); initialEstimate.insertPose(1, x1);
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Pose2 x2(2.3, 0.1,-0.2 ); initialEstimate.insertPose(2, x2);
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Pose2 x3(4.1, 0.1, M_PI_2); initialEstimate.insertPose(3, x3);
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Pose2 x4(4.0, 2.0, M_PI ); initialEstimate.insertPose(4, x4);
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Pose2 x5(2.1, 2.1,-M_PI_2); initialEstimate.insertPose(5, x5);
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initialEstimate.print("\nInitial estimate:\n ");
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cout << "initial error = " << graph.error(initialEstimate) << endl ;
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// 4. Single Step Optimization using Levenberg-Marquardt
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LevenbergMarquardtParams param;
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param.linearSolverType = SuccessiveLinearizationParams::CG;
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param.iterativeParams = boost::make_shared<IterativeOptimizationParameters>();
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LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, param);
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Values result = optimizer.optimize();
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cout << "final error = " << graph.error(result) << endl;
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return 0 ;
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}
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