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										 |  |  | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | 
					
						
							|  |  |  | % GTSAM Copyright 2010, Georgia Tech Research Corporation,  | 
					
						
							|  |  |  | % Atlanta, Georgia 30332-0415 | 
					
						
							|  |  |  | % All Rights Reserved | 
					
						
							|  |  |  | % Authors: Frank Dellaert, et al. (see THANKS for the full author list) | 
					
						
							|  |  |  | %  | 
					
						
							|  |  |  | % See LICENSE for the license information | 
					
						
							|  |  |  | % | 
					
						
							|  |  |  | % @brief Example of a simple 2D localization example | 
					
						
							|  |  |  | % @author Frank Dellaert | 
					
						
							|  |  |  | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | 
					
						
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							|  |  |  | %% Assumptions | 
					
						
							|  |  |  | %  - Robot poses are facing along the X axis (horizontal, to the right in 2D) | 
					
						
							|  |  |  | %  - The robot moves 2 meters each step | 
					
						
							|  |  |  | %  - The robot is on a grid, moving 2 meters each step | 
					
						
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							|  |  |  | %% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph) | 
					
						
							|  |  |  | graph = pose2SLAMGraph; | 
					
						
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							|  |  |  | %% Add two odometry factors | 
					
						
							|  |  |  | odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case) | 
					
						
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										 |  |  | odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta | 
					
						
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										 |  |  | graph.addRelativePose(1, 2, odometry, odometryNoise); | 
					
						
							|  |  |  | graph.addRelativePose(2, 3, odometry, odometryNoise); | 
					
						
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							|  |  |  | %% Add three "GPS" measurements | 
					
						
							|  |  |  | % We use Pose2 Priors here with high variance on theta | 
					
						
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										 |  |  | noiseModel = gtsamnoiseModelDiagonal.Sigmas([0.1; 0.1; 10]); | 
					
						
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										 |  |  | graph.addPosePrior(1, gtsamPose2(0.0, 0.0, 0.0), noiseModel); | 
					
						
							|  |  |  | graph.addPosePrior(2, gtsamPose2(2.0, 0.0, 0.0), noiseModel); | 
					
						
							|  |  |  | graph.addPosePrior(3, gtsamPose2(4.0, 0.0, 0.0), noiseModel); | 
					
						
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							|  |  |  | %% print | 
					
						
							|  |  |  | graph.print(sprintf('\nFactor graph:\n')); | 
					
						
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							|  |  |  | %% Initialize to noisy points | 
					
						
							|  |  |  | initialEstimate = pose2SLAMValues; | 
					
						
							|  |  |  | initialEstimate.insertPose(1, gtsamPose2(0.5, 0.0, 0.2)); | 
					
						
							|  |  |  | initialEstimate.insertPose(2, gtsamPose2(2.3, 0.1,-0.2)); | 
					
						
							|  |  |  | initialEstimate.insertPose(3, gtsamPose2(4.1, 0.1, 0.1)); | 
					
						
							|  |  |  | initialEstimate.print(sprintf('\nInitial estimate:\n  ')); | 
					
						
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							|  |  |  | %% Optimize using Levenberg-Marquardt optimization with an ordering from colamd | 
					
						
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										 |  |  | result = graph.optimize(initialEstimate,1); | 
					
						
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										 |  |  | result.print(sprintf('\nFinal result:\n  ')); | 
					
						
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							|  |  |  | %% Plot Covariance Ellipses | 
					
						
							|  |  |  | cla; | 
					
						
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										 |  |  | X=result.poses(); | 
					
						
							|  |  |  | plot(X(:,1),X(:,2),'k*-'); hold on | 
					
						
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										 |  |  | marginals = graph.marginals(result); | 
					
						
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										 |  |  | P={}; | 
					
						
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										 |  |  | for i=1:result.size() | 
					
						
							|  |  |  |     pose_i = result.pose(i); | 
					
						
							|  |  |  |     P{i}=marginals.marginalCovariance(i); | 
					
						
							|  |  |  |     plotPose2(pose_i,'g',P{i}) | 
					
						
							|  |  |  | end | 
					
						
							|  |  |  | axis([-0.6 4.8 -1 1]) | 
					
						
							|  |  |  | axis equal | 
					
						
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										 |  |  | view(2) |