77 lines
2.1 KiB
Matlab
77 lines
2.1 KiB
Matlab
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% Set up a small SLAM example in MATLAB to test the execution time
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clear;
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clf;
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%Parameters
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noRuns=100;
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steps=1;
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m = 5;
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velocity=1;
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time_qr=[];
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time_gtsam=[];
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for steps=1:noRuns
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%figure(1);clf;
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% robot moves in the world
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trajectory = walk([0.1,0.1],velocity,m);
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mappingArea=max(trajectory,[],2);
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%plot(trajectory(1,:),trajectory(2,:),'b+'); hold on;
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visibilityTh=sqrt(mappingArea(1)^2+mappingArea(2)^2)/m; %distance between poses
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% Set up the map
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map = create_landmarks(visibilityTh, mappingArea,steps);
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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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n=size(map,1)*size(map,2);
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% Check visibility and plot this on the problem figure
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visibilityTh=visibilityTh+steps;
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visibility = create_visibility(map, trajectory,visibilityTh);
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%gplot(visibility,[map trajectory]');
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steps
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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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gaussianFactorGraph = create_gaussian_factor_graph(config, measurements, odometry, measurement_sigma, odo_sigma, n);
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%
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% create an ordering
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ord = create_ordering(n,m);
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ijs = gaussianFactorGraph.sparse(ord);
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A=sparse(ijs(1,:),ijs(2,:),ijs(3,:));
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runs=50; % for each graph run QR and elimination several times and average the time
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ck_qr=cputime;
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for it=1:runs
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R_qr = qr(A);
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end
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time_qr=[time_qr,(cputime-ck_qr)/runs];
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% eliminate with that ordering
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%time gt_sam
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for it=1:runs+1
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if it==2
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ck_gt=cputime;
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end
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BayesNet = gaussianFactorGraph.eliminate_(ord);
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end
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time_gtsam=[time_gtsam,(cputime-ck_gt)/runs];
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clear trajectory visibility gaussianFactorGraph measurements odometry;
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m = m+5;
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velocity=velocity+1;
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steps=steps+1;
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end
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plot(time_qr,'r');hold on;
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plot(time_gtsam,'b');
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