62 lines
1.1 KiB
Matlab
62 lines
1.1 KiB
Matlab
% create a linear factor graph
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% The non-linear graph above evaluated at NoisyConfig
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function fg = createGaussianFactorGraph()
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c = createNoisyConfig();
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% Create
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fg = GaussianFactorGraph;
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sigma1=.1;
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% prior on x1
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A11=eye(2);
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b = - c.get('x1');
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<<<<<<< .mine
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f1 = LinearFactor('x1', A11, b, sigma1);
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=======
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f1 = GaussianFactor('x1', A11, b);
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>>>>>>> .r1017
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fg.push_back(f1);
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% odometry between x1 and x2
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sigma2=.1;
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<<<<<<< .mine
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A21=-eye(2);
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A22=eye(2);
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b = [.2;-.1];
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f2 = LinearFactor('x1', A21, 'x2', A22, b,sigma2);
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=======
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f2 = GaussianFactor('x1', A21, 'x2', A22, b);
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>>>>>>> .r1017
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fg.push_back(f2);
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% measurement between x1 and l1
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sigma3=.2;
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A31=-eye(2);
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A33=eye(2);
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b = [0;.2];
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<<<<<<< .mine
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f3 = LinearFactor('x1', A31, 'l1', A33, b,sigma3);
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=======
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f3 = GaussianFactor('x1', A31, 'l1', A32, b);
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>>>>>>> .r1017
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fg.push_back(f3);
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% measurement between x2 and l1
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sigma4=.2;
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A42=-eye(2);
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A43=eye(2);
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b = [-.2;.3];
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<<<<<<< .mine
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f4 = LinearFactor('x2', A42, 'l1', A43, b,sigma4);
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=======
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f4 = GaussianFactor('x2', A41, 'l1', A42, b);
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>>>>>>> .r1017
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fg.push_back(f4);
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end |