102 lines
3.4 KiB
C++
102 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 LocalizationExample.cpp
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* @brief Simple robot localization example, with three "GPS-like" 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/LevenbergMarquardtOptimizer.h>
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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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using namespace gtsam::noiseModel;
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/**
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* UnaryFactor
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* Example on how to create a GPS-like factor on position alone.
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*/
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class UnaryFactor: public NoiseModelFactor1<Pose2> {
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double mx_, my_; ///< X and Y measurements
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public:
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UnaryFactor(Key j, double x, double y, const SharedNoiseModel& model):
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NoiseModelFactor1<Pose2>(model, j), mx_(x), my_(y) {}
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virtual ~UnaryFactor() {}
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Vector evaluateError(const Pose2& q,
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boost::optional<Matrix&> H = boost::none) const
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{
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if (H) (*H) = Matrix_(2,3, 1.0,0.0,0.0, 0.0,1.0,0.0);
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return Vector_(2, q.x() - mx_, q.y() - my_);
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}
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};
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/**
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* Example of a more complex 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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* - We have unary measurement factors at eacht time step
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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 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 = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
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graph.addRelativePose(1, 2, odometry, odometryNoise);
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graph.addRelativePose(2, 3, odometry, odometryNoise);
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// add unary measurement factors, like GPS, on all three poses
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SharedDiagonal noiseModel = Diagonal::Sigmas(Vector_(2, 0.1, 0.1)); // 10cm std on x,y
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graph.push_back(boost::make_shared<UnaryFactor>(1, 0, 0, noiseModel));
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graph.push_back(boost::make_shared<UnaryFactor>(2, 2, 0, noiseModel));
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graph.push_back(boost::make_shared<UnaryFactor>(3, 4, 0, noiseModel));
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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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// use an explicit Optimizer object
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LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
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pose2SLAM::Values result = optimizer.optimize();
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result.print("\nFinal result:\n ");
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// Query the marginals
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Marginals marginals(graph, result);
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cout.precision(2);
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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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