83 lines
2.7 KiB
C++
83 lines
2.7 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 Pose2SLAMExample_advanced.cpp
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* @brief Simple Pose2SLAM Example using
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* pre-built pose2SLAM domain
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* @author Chris Beall
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*/
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// pull in the Pose2 SLAM domain with all typedefs and helper functions defined
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#include <gtsam/slam/pose2SLAM.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/Marginals.h>
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#include <gtsam/base/Vector.h>
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#include <gtsam/base/Matrix.h>
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#include <boost/shared_ptr.hpp>
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#include <cmath>
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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using namespace gtsam::noiseModel;
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int main(int argc, char** argv) {
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/* 1. create graph container and add factors to it */
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pose2SLAM::Graph graph;
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/* 2.a add prior */
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Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
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SharedDiagonal priorNoise = Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
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graph.addPosePrior(1, priorMean, priorNoise); // add directly to graph
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/* 2.b add odometry */
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SharedDiagonal odometryNoise = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
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Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
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graph.addRelativePose(1, 2, odometry, odometryNoise);
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graph.addRelativePose(2, 3, odometry, odometryNoise);
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graph.print("full graph");
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/* 3. Create the data structure to hold the initial estimate to the solution
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* initialize to noisy points */
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pose2SLAM::Values initial;
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initial.insertPose(1, Pose2(0.5, 0.0, 0.2));
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initial.insertPose(2, Pose2(2.3, 0.1, -0.2));
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initial.insertPose(3, Pose2(4.1, 0.1, 0.1));
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initial.print("initial estimate");
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/* 4.2.1 Alternatively, you can go through the process step by step
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* Choose an ordering */
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Ordering ordering = *graph.orderingCOLAMD(initial);
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/* 4.2.2 set up solver and optimize */
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LevenbergMarquardtParams params;
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params.absoluteErrorTol = 1e-15;
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params.relativeErrorTol = 1e-15;
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params.ordering = ordering;
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LevenbergMarquardtOptimizer optimizer(graph, initial, params);
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pose2SLAM::Values result = optimizer.optimize();
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result.print("final result");
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/* Get covariances */
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Marginals marginals(graph, result, Marginals::CHOLESKY);
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Matrix covariance1 = marginals.marginalCovariance(1);
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Matrix covariance2 = marginals.marginalCovariance(2);
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print(covariance1, "Covariance1");
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print(covariance2, "Covariance2");
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return 0;
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}
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