59 lines
1.6 KiB
Matlab
59 lines
1.6 KiB
Matlab
% Set up a small SLAM example
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% Christian Potthast, Frank Dellaert
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clear;
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close all;
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n = 100;
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m = 20;
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% have the robot move in this world
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trajectory = random_walk([0.1,0.1],5,m);
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plot(trajectory(1,:),trajectory(2,:),'b+');
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axis([0 100 0 100]);axis square;
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% Set up the map
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mappingArea=max(trajectory,[],2);
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map = create_random_landmarks(n, mappingArea);
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figure(1);clf;
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plot(map(1,:), map(2,:),'g.'); hold on;
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axis([0 mappingArea(1) 0 mappingArea(2)]);axis square;
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% Check visibility and plot this on the problem figure
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visibility = create_visibility(map, trajectory,10);
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gplot(visibility,[map trajectory]');
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% simulate the measurements
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measurement_sigma = 1;
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odo_sigma = 0.1;
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[measurements, odometry] = simulate_measurements(map, trajectory, visibility, measurement_sigma, odo_sigma);
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% create a configuration of all zeroes
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config = create_config(n,m);
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% create the factor graph
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factorGraph = create_gaussian_factor_graph(config, measurements, odometry, measurement_sigma, odo_sigma, n);
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% create an ordering
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ord = create_ordering(n,m);
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% show the matrix
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figure(2); clf;
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A = factorGraph.matrix(ord);
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spy(A);
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% optimizing a BayesNet is not possible from MATLAB as
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% GaussianBayesNet is a typedef not a real class :-(
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% BayesNet = factorGraph.eliminate_(ord);
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% optimal = BayesNet.optimize;
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% However, we can call the optimize_ method of a GaussianFactorGraph
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optimal = factorGraph.optimize_(ord);
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% plot the solution
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figure(3);clf;
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plot_config(optimal,n,m);hold on
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plot(trajectory(1,:),trajectory(2,:),'b+');
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plot(map(1,:), map(2,:),'g.');
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axis([0 mappingArea(1) 0 mappingArea(2)]);axis square;
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