% create a linear factor graph
% The non-linear graph above evaluated at NoisyConfig
function fg = createGaussianFactorGraph()

c = createNoisyConfig();

% Create
fg = GaussianFactorGraph;
sigma1=.1;

% prior on x1
A11=eye(2);
b = - c.get('x1');

<<<<<<< .mine
f1 = LinearFactor('x1', A11, b, sigma1);
=======
f1 = GaussianFactor('x1', A11, b);
>>>>>>> .r1017
fg.push_back(f1);

% odometry between x1 and x2
sigma2=.1;

<<<<<<< .mine
A21=-eye(2);
A22=eye(2);
b = [.2;-.1];

f2 = LinearFactor('x1', A21,  'x2', A22, b,sigma2);
=======
f2 = GaussianFactor('x1', A21,  'x2', A22, b);
>>>>>>> .r1017
fg.push_back(f2);

% measurement between x1 and l1
sigma3=.2;
A31=-eye(2);
A33=eye(2);
b = [0;.2];

<<<<<<< .mine
f3 = LinearFactor('x1', A31, 'l1', A33, b,sigma3);
=======
f3 = GaussianFactor('x1', A31, 'l1', A32, b);
>>>>>>> .r1017
fg.push_back(f3);

% measurement between x2 and l1
sigma4=.2;
A42=-eye(2);
A43=eye(2);
b = [-.2;.3];

<<<<<<< .mine
f4 = LinearFactor('x2', A42, 'l1', A43, b,sigma4);
=======
f4 = GaussianFactor('x2', A41, 'l1', A42, b);
>>>>>>> .r1017
fg.push_back(f4);

end