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220 lines
6.1 KiB
Plaintext
220 lines
6.1 KiB
Plaintext
/*
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* icp6Dlumquat implementation
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*
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* Copyright (C) Andreas Nuechter, Alexandru-Eugen Ichim
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*
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* Released under the GPL version 3.
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*
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*/
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/** @file
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* @brief Implementation of the ICP error function minimization via
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* via linearization with quaternions
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*
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* @author Andreas Nuechter. Jacobs University Bremen gGmbH, Germany
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* @author Alexandru-Eugen Ichim. Jacobs University Bremen gGmbH, Germany
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*
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*/
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#include "slam6d/icp6Dlumquat.h"
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#include "slam6d/globals.icc"
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#include <iomanip>
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using std::ios;
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using std::resetiosflags;
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using std::setiosflags;
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#include "newmat/newmat.h"
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#include "newmat/newmatap.h"
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using namespace NEWMAT;
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/**
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* computes the rotation matrix consisting
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* of a rotation and translation that
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* minimizes the root-mean-square error of the
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* point pairs using linearization with quarernions
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* @param pairs Vector of point pairs (pairs of corresponding points)
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* @param *alignfx The resulting transformation matrix
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* @return Error estimation of the matching (rms)
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*/
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double icp6D_LUMQUAT::Point_Point_Align(const vector<PtPair>& pairs, double *alignfx,
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const double centroid_m[3], const double centroid_d[3])
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{
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// alignfx is filled with the current pose, t is the translation, quat is the quaternion
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double t[3], quat[4];
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Matrix4ToQuat(alignfx, quat, t);
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double error = 0;
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double sum = 0.0;
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for(unsigned int i = 0; i < pairs.size(); i++){
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sum += sqr(pairs[i].p1.x - pairs[i].p2.x)
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+ sqr(pairs[i].p1.y - pairs[i].p2.y)
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+ sqr(pairs[i].p1.z - pairs[i].p2.z) ;
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}
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error = sqrt(sum / (double)pairs.size());
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if (!quiet) {
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cout.setf(ios::basefield);
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cout << "LUMQUAT RMS point-to-point error = "
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<< resetiosflags(ios::adjustfield) << setiosflags(ios::internal)
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<< resetiosflags(ios::floatfield) << setiosflags(ios::fixed)
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<< std::setw(10) << std::setprecision(7)
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<< error
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<< " using " << std::setw(6) << (int)pairs.size() << " points" << endl;
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}
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/////////////////////////////////////////////////////////
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/////////////////////////////////////////////////////////
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/// MZ = M^T * Z
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ColumnVector MZ(7); MZ = 0.0;
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/// MM = M^T * M
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Matrix MM(7, 7); MM = 0.0;
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double x = 0.0, y = 0.0, z = 0.0, dx = 0.0, dy = 0.0, dz = 0.0, sx = 0.0, sy = 0.0, sz = 0.0, xpy = 0.0, xpz = 0.0, ypz = 0.0, xpypz = 0.0, xy = 0.0, yz = 0.0, xz = 0.0;
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for(unsigned int i = 0; i < pairs.size(); ++i) {
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/// temporary values that we shall use multiple times in the subsequent computations
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x = (pairs[i].p1.x + pairs[i].p1.x) / 2.0;
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y = (pairs[i].p1.y + pairs[i].p2.y) / 2.0;
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z = (pairs[i].p1.z + pairs[i].p2.z) / 2.0;
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dx = pairs[i].p1.x - pairs[i].p2.x;
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dy = pairs[i].p1.y - pairs[i].p2.y;
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dz = pairs[i].p1.z - pairs[i].p2.z;
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/// sums of each coordinate
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sx += x;
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sy += y;
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sz += z;
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/// sums of squares of coordinates
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xpy += x*x + y*y;
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xpz += x*x + z*z;
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ypz += y*y + z*z;
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xpypz += x*x + y*y + z*z;
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/// sums of products of pairs of coordinates
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xy += x*y;
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xz += x*z;
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yz += y*z;
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/// incrementally construct matrix M^T * Z
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MZ(1) += dx;
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MZ(2) += dy;
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MZ(3) += dz;
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MZ(4) += x*dx + y*dy + z*dz;
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MZ(5) += z*dy - y*dz;
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MZ(6) += x*dz - z*dx;
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MZ(7) += y*dx - x*dy;
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}
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/// construct M^ * M
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MM(1,1) = MM(2,2) = MM(3,3) = pairs.size();
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MM(4,4) = xpypz;
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MM(5,5) = ypz;
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MM(6,6) = xpz;
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MM(7,7) = xpy;
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MM(1,4) = MM(4,1) = sx;
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MM(1,6) = MM(6,1) = -sz;
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MM(1,7) = MM(7,1) = sy;
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MM(2,4) = MM(4,2) = sy;
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MM(2,5) = MM(5,2) = sz;
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MM(2,7) = MM(7,2) = -sx;
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MM(3,4) = MM(4,3) = sz;
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MM(3,5) = MM(5,3) = -sy;
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MM(3,6) = MM(6,3) = sx;
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MM(5,6) = MM(6,5) = -xy;
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MM(5,7) = MM(7,5) = -xz;
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MM(6,7) = MM(7,6) = -yz;
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ColumnVector Ehat = MM.i() * MZ ;
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/// construct the auxiliary matrices U and T needed to build up H
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double p = quat[0], q = quat[1], r = quat[2], s = quat[3];
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x = t[0], y = t[1], z = t[2];
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Matrix U(4, 4);
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U << p << q << r << s
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<< q <<-p << s <<-r
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<< r <<-s <<-p << q
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<< s << r <<-q <<-p;
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Matrix T(3, 4);
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T << p*x + s*y - r*z << q*x + r*y + s*z << r*x - q*y + p*z << s*x - p*y - q*z
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<<-s*x + p*y + q*z <<-r*x + q*y - p*z << q*x + r*y + s*z << p*x + s*y - r*z
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<< r*x - q*y + p*z <<-s*x + p*y + q*z <<-p*x - s*y + r*z << q*x + r*y - s*z;
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/// now actually compose matrix H
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Matrix H(7, 7); H = 0.0;
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H.SubMatrix(1, 3, 1, 3) = IdentityMatrix(3);
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H.SubMatrix(1, 3, 4, 7) = T *(-2);
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H.SubMatrix(4, 7, 4, 7) = U * 2;
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/// Xhat is the pose estimate for the second scan - use the pose of the first scan
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ColumnVector Xhat(7); Xhat << x << y << z << p << q << r << s;
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/// create a transformation matrix for the first scan with the given values
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Matrix T1(4, 4); T1 = 0.0;
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Matrix Cq(3, 3);
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ColumnVector qvec(3); qvec << q << r << s;
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Cq << 0 << -qvec(3) << qvec(2) << qvec(3) << 0 << -qvec(1) << -qvec(2) << qvec(1) << 0;
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ColumnVector qtq(1); qtq = qvec.t() *qvec * (-1);
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T1.SubMatrix(1, 3, 1, 3) = IdentityMatrix(3) * (p*p + qtq(1)) + qvec*qvec.t()*2 + Cq *2*p;
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T1(1, 4) = x;
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T1(2, 4) = y;
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T1(3, 4) = z;
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T1(4, 4) = 1;
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/// the translation and quaternion for the second scan are computed
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ColumnVector X = Xhat - H.i() * Ehat;
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/// create the transformation matrix for the second scan
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x = X(1), y = X(2), z = X(3);
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p = X(4); q = X(5); r = X(6); s = X(7);
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Matrix T2(4, 4); T2 = 0.0;
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qvec = 0.0; qvec << q << r << s;
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Cq = 0.0; Cq << 0 << -qvec(3) << qvec(2) << qvec(3) << 0 << -qvec(1) << -qvec(2) << qvec(1) << 0;
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qtq = qvec.t() *qvec * (-1);
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T2.SubMatrix(1, 3, 1, 3) = IdentityMatrix(3) * (p*p + qtq(1)) + qvec*qvec.t()*2 + Cq *2*p;
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T2(1, 4) = x;
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T2(2, 4) = y;
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T2(3, 4) = z;
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T2(4, 4) = 1;
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/// the incremental transform calculated from the absolute poses of the two scans
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Matrix T_inc = T1 * T2.i();
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/// convert our 4x4 transform to column-wise opengl form
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alignfx[0] = T_inc(1, 1);
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alignfx[1] = T_inc(2, 1);
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alignfx[2] = T_inc(3, 1);
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alignfx[3] = 0.0;
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alignfx[4] = T_inc(1, 2);
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alignfx[5] = T_inc(2, 2);
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alignfx[6] = T_inc(3, 2);
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alignfx[7] = 0.0;
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alignfx[8] = T_inc(1, 3);
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alignfx[9] = T_inc(2, 3);
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alignfx[10]= T_inc(3, 3);
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alignfx[11]= 0.0;
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alignfx[12]= T_inc(1, 4);
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alignfx[13]= T_inc(2, 4);
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alignfx[14]= T_inc(3, 4);
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alignfx[15]= 1.0;
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/////////////////////////////////////////////////////////
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/////////////////////////////////////////////////////////
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return error;
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}
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