77 lines
		
	
	
		
			2.1 KiB
		
	
	
	
		
			Matlab
		
	
	
			
		
		
	
	
			77 lines
		
	
	
		
			2.1 KiB
		
	
	
	
		
			Matlab
		
	
	
| % Set up a small SLAM example in MATLAB to test the execution time
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| 
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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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|  
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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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| 
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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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|     
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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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|     
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|     
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|     % create a configuration of all zeroes
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|      config = create_config(n,m);
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| 
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|     % create the factor graph
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|     linearFactorGraph = create_linear_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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| 
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|     ijs = linearFactorGraph.sparse(ord);
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|     A=sparse(ijs(1,:),ijs(2,:),ijs(3,:)); 
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| 
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|     
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|     runs=50; % for each graph run QR and elimination several times and average the time
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|     
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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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|     
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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 = linearFactorGraph.eliminate_(ord); 
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|     end
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|     time_gtsam=[time_gtsam,(cputime-ck_gt)/runs];
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|     
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|     clear trajectory visibility linearFactorGraph 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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| 
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