82 lines
3.3 KiB
C++
82 lines
3.3 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 Pose2SLAMwSPCG.cpp
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* @brief A 2D Pose SLAM example using the SimpleSPCGSolver.
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* @author Yong-Dian Jian
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* @date June 2, 2012
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*/
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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/LevenbergMarquardtOptimizer.h>
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// In contrast to that example, however, we will use a PCG solver here
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#include <gtsam/linear/SubgraphSolver.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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NonlinearFactorGraph graph;
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// 2a. Add a prior on the first pose, setting it to the origin
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Pose2 prior(0.0, 0.0, 0.0); // prior at 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.push_back(PriorFactor<Pose2>(1, prior, priorNoise));
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// 2b. Add odometry factors
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
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graph.push_back(BetweenFactor<Pose2>(1, 2, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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graph.push_back(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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graph.push_back(BetweenFactor<Pose2>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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graph.push_back(BetweenFactor<Pose2>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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// 2c. Add the loop closure constraint
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noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
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graph.push_back(BetweenFactor<Pose2>(5, 1, Pose2(0.0, 0.0, 0.0), 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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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, 1.1));
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initialEstimate.insert(3, Pose2(2.1, 1.9, 2.8));
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initialEstimate.insert(4, Pose2(-.3, 2.5, 4.2));
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initialEstimate.insert(5, Pose2(0.1,-0.7, 5.8));
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initialEstimate.print("\nInitial Estimate:\n"); // print
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// 4. Single Step Optimization using Levenberg-Marquardt
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LevenbergMarquardtParams parameters;
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parameters.verbosity = NonlinearOptimizerParams::ERROR;
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parameters.verbosityLM = LevenbergMarquardtParams::LAMBDA;
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// LM is still the outer optimization loop, but by specifying "Iterative" below
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// We indicate that an iterative linear solver should be used.
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// In addition, the *type* of the iterativeParams decides on the type of
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// iterative solver, in this case the SPCG (subgraph PCG)
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parameters.linearSolverType = NonlinearOptimizerParams::Iterative;
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parameters.iterativeParams = boost::make_shared<SubgraphSolverParameters>();
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LevenbergMarquardtOptimizer 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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cout << "subgraph solver final error = " << graph.error(result) << endl;
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return 0;
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}
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