Made sure worked with non-diagonal noise model
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4225f37846
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fb3a959193
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@ -124,7 +124,7 @@ TEST( KalmanFilter, predict ) {
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Matrix F = Matrix_(2,2, 1.0,0.1, 0.2,1.1);
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Matrix B = Matrix_(2,3, 1.0,0.1,0.2, 1.1,1.2,0.8);
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Vector u = Vector_(3, 1.0, 0.0, 2.0);
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Matrix R = Matrix_(2,2, 1.0,0.0, 0.0,3.0);
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Matrix R = Matrix_(2,2, 1.0,0.5, 0.0,3.0);
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Matrix M = trans(R)*R;
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Matrix Q = inverse(M);
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@ -138,7 +138,7 @@ TEST( KalmanFilter, predict ) {
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// Ensure predictQ and predict2 give same answer for non-trivial inputs
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kalmanFilter1.predictQ(F, B, u, Q);
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// We have A1 = -F, A2 = I_, b = B*u:
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// We have A1 = -F, A2 = I_, b = B*u, pre-multipled with R to match Q noise model
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Matrix A1 = -R*F, A2 = R;
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Vector b = R*B*u;
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SharedDiagonal nop = noiseModel::Isotropic::Sigma(2, 1.0);
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