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84 lines
2.0 KiB
Matlab
84 lines
2.0 KiB
Matlab
function correctedData = testICP(anglex, angley, anglez)
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% erstellung der testrotation, Winkelangabe in grad
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Rz = [cos(anglez) -sin(anglez) 0;
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sin(anglez) cos(anglez) 0;
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0 0 1];
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Ry = [ cos(angley) 0 sin(angley);
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0 1 0;
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-sin(angley) 0 cos(angley)];
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Rx = [1 0 0;
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0 cos(anglex) -sin(anglex);
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0 sin(anglex) cos(anglex)];
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R = Rz * Ry * Rx;
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%% generate random model
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model = rand(3, 10)*100
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%% generate noise
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noise = rand(3, 10)*5;
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% rotate and translate model to use as data to be matched
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%data = R * model + 200;
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% rotate and translate model to use as data to be matched and add noise
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data = R * model + 200 + noise;
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%% center model cloud
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cm = sum(model')' / size(model,2);
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centerModel = model - repmat(cm, 1, size(model,2));
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%% center data cloud
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cd = sum(data')' / size(data,2);
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centerData = data - repmat(cd, 1, size(data,2));
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%% correlation matrix H
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H = zeros(3,3);
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for i=1:size(data,2)
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for j=1:3
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for k=1:3
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H(j,k) = H(j,k) + centerModel(j,i)*centerData(k,i);
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end
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end
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end
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% H = zeros(3,3);
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% for i=1:3
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% for j=1:3
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% H(i,j) = model(i,:) *data(j,:)';
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% end
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% end
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%% rotation
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% error in the paper: not H*H' but H'*H!
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HH = H'*H;
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[eVec, eVal] = eig(HH);
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lamb1=eVal(1,1);
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lamb2=eVal(2,2);
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lamb3=eVal(3,3);
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u1=eVec(:,1);
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u2=eVec(:,2);
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u3=eVec(:,3);
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R=H*(1/sqrt(lamb1)*(u1*u1') + 1/sqrt(lamb2)*(u2*u2') + 1/sqrt(lamb3)*(u3*u3'))
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%% translation
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t=(cm - R*cd)
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%% transform data to match the model
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transformeddata = R*data + repmat(t, 1, size(data,2))
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%%plot model and transformed data
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plot3(transformeddata(1,:),transformeddata(2,:),transformeddata(3,:), 'r.', model(1,:),model(2,:),model(3,:),'go')
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legend ('transformed data', 'model', 'Location','NorthEast')
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end
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