80 lines
1.3 KiB
Matlab
80 lines
1.3 KiB
Matlab
%-----------------------------------------------------------------------
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% eliminate
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import gtsam.*
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% the combined linear factor
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Ax2 = [
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-5., 0.
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+0.,-5.
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10., 0.
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+0.,10.
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];
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Al1 = [
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5., 0.
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0., 5.
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0., 0.
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0., 0.
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];
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Ax1 = [
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0.00, 0. % f4
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0.00, 0. % f4
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-10., 0. % f2
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0.00,-10. % f2
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];
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x2 = 1;
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l1 = 2;
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x1 = 3;
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% the RHS
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b2=[-1;1.5;2;-1];
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sigmas = [1;1;1;1];
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model4 = noiseModel.Diagonal.Sigmas(sigmas);
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combined = JacobianFactor(x2, Ax2, l1, Al1, x1, Ax1, b2, model4);
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% eliminate the first variable (x2) in the combined factor, destructive !
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actualCG = combined.eliminateFirst();
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% create expected Conditional Gaussian
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R11 = [
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11.1803, 0.00
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0.00, 11.1803
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];
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S12 = [
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-2.23607, 0.00
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+0.00,-2.23607
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];
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S13 = [
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-8.94427, 0.00
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+0.00,-8.94427
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];
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d=[2.23607;-1.56525];
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expectedCG = GaussianConditional(x2,d,R11,l1,S12,x1,S13,[1;1]);
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% check if the result matches
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CHECK('actualCG.equals(expectedCG,1e-5)',actualCG.equals(expectedCG,1e-4));
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% the expected linear factor
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Bl1 = [
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4.47214, 0.00
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0.00, 4.47214
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];
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Bx1 = [
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% x1
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-4.47214, 0.00
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+0.00, -4.47214
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];
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% the RHS
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b1= [0.0;0.894427];
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model2 = noiseModel.Diagonal.Sigmas([1;1]);
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expectedLF = JacobianFactor(l1, Bl1, x1, Bx1, b1, model2);
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% check if the result matches the combined (reduced) factor
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% FIXME: JacobianFactor/GaussianFactor mismatch
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%CHECK('combined.equals(expectedLF,1e-5)',combined.equals(expectedLF,1e-4));
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