54 lines
		
	
	
		
			1.6 KiB
		
	
	
	
		
			C++
		
	
	
		
		
			
		
	
	
			54 lines
		
	
	
		
			1.6 KiB
		
	
	
	
		
			C++
		
	
	
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								/* ----------------------------------------------------------------------------
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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_graph->cpp
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								 * @brief Read graph from file and perform GraphSLAM
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								 * @date June 3, 2012
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								 * @author Frank Dellaert
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								 */
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								#include <gtsam/slam/dataset.h>
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								#include <gtsam/slam/pose2SLAM.h>
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								#include <gtsam/nonlinear/Marginals.h>
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								#include <boost/tuple/tuple.hpp>
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								#include <cmath>
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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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									// Read File and create graph and initial estimate
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									// we are in build/examples, data is in examples/Data
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									pose2SLAM::Graph::shared_ptr graph ;
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									pose2SLAM::Values::shared_ptr initial;
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									SharedDiagonal model(Vector_(3, 0.05, 0.05, 5.0*M_PI/180.0));
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									boost::tie(graph,initial) = load2D("../../examples/Data/w100-odom.graph",model);
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									initial->print("Initial estimate:\n");
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									// Add a Gaussian prior on first poses
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									Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
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									SharedDiagonal priorNoise(Vector_(3, 0.01, 0.01, 0.01));
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									graph->addPrior(0, priorMean, priorNoise);
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									// Single Step Optimization using Levenberg-Marquardt
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									pose2SLAM::Values result = graph->optimize(*initial);
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									result.print("\nFinal result:\n");
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									// Plot the covariance of the last pose
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									Marginals marginals(*graph, result);
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									cout.precision(2);
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								  cout << "\nP3:\n" << marginals.marginalCovariance(99) << endl;
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								return 0;
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								}
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