76 lines
2.5 KiB
C++
76 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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/**
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* @file OdometryExample.cpp
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* @brief Simple robot motion example, with prior and two odometry measurements
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* @author Frank Dellaert
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*/
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// pull in the 2D PoseSLAM domain with all typedefs and helper functions defined
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#include <gtsam/slam/pose2SLAM.h>
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// include this for marginals
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#include <gtsam/nonlinear/Marginals.h>
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#include <iomanip>
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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/**
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* Example of a simple 2D localization example
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* - Robot poses are facing along the X axis (horizontal, to the right in 2D)
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* - The robot moves 2 meters each step
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* - We have full odometry between poses
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*/
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int main(int argc, char** argv) {
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// create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
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pose2SLAM::Graph graph;
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// add a Gaussian prior on pose x_1
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Pose2 priorMean(0.0, 0.0, 0.0); // prior mean is at origin
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SharedDiagonal priorNoise(Vector_(3, 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
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graph.addPrior(1, priorMean, priorNoise); // add directly to graph
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// add two odometry factors
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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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SharedDiagonal odometryNoise(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
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graph.addOdometry(1, 2, odometry, odometryNoise);
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graph.addOdometry(2, 3, odometry, odometryNoise);
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// print
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graph.print("\nFactor graph:\n");
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// create (deliberatly inaccurate) initial estimate
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pose2SLAM::Values initialEstimate;
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initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insertPose(2, Pose2(2.3, 0.1, -0.2));
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initialEstimate.insertPose(3, Pose2(4.1, 0.1, 0.1));
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initialEstimate.print("\nInitial estimate:\n ");
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// optimize using Levenberg-Marquardt optimization with an ordering from colamd
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pose2SLAM::Values result = graph.optimize(initialEstimate);
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result.print("\nFinal result:\n ");
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// Query the marginals
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cout.precision(2);
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Marginals marginals = graph.marginals(result);
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cout << "\nP1:\n" << marginals.marginalCovariance(1) << endl;
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cout << "\nP2:\n" << marginals.marginalCovariance(2) << endl;
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cout << "\nP3:\n" << marginals.marginalCovariance(3) << endl;
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return 0;
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}
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