89 lines
2.9 KiB
C++
89 lines
2.9 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.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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#include <cmath>
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#include <iostream>
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#include <boost/shared_ptr.hpp>
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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/NonlinearOptimization-inl.h>
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#include <gtsam/base/Vector.h>
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#include <gtsam/base/Matrix.h>
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using namespace std;
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using namespace gtsam;
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using namespace boost;
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using namespace gtsam::pose2SLAM;
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int main(int argc, char** argv) {
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// create keys for robot positions
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Key x1(1), x2(2), x3(3);
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/* 1. create graph container and add factors to it */
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shared_ptr<Graph> graph(new Graph);
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/* 2.a add prior */
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// gaussian for prior
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SharedDiagonal prior_model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
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Pose2 prior_measurement(0.0, 0.0, 0.0); // prior at origin
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graph->addPrior(x1, prior_measurement, prior_model); // add directly to graph
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/* 2.b add odometry */
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// general noisemodel for odometry
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SharedDiagonal odom_model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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/* Pose2 measurements take (x,y,theta), where theta is taken from the positive x-axis*/
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Pose2 odom_measurement(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
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graph->addConstraint(x1, x2, odom_measurement, odom_model);
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graph->addConstraint(x2, x3, odom_measurement, odom_model);
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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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shared_ptr<Values> initial(new Values);
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initial->insert(x1, Pose2(0.5, 0.0, 0.2));
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initial->insert(x2, Pose2(2.3, 0.1,-0.2));
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initial->insert(x3, 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::shared_ptr ordering = graph->orderingCOLAMD(*initial);
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/* 4.2.2 set up solver and optimize */
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Optimizer optimizer(graph, initial, ordering);
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Optimizer::Parameters::verbosityLevel verbosity = pose2SLAM::Optimizer::Parameters::SILENT;
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Optimizer optimizer_result = optimizer.levenbergMarquardt(1e-15, 1e-15, verbosity);
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Values result = *optimizer_result.values();
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result.print("final result");
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/* Get covariances */
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Matrix covariance1 = optimizer_result.marginalCovariance(x1).second;
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Matrix covariance2 = optimizer_result.marginalCovariance(x2).second;
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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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