459 lines
13 KiB
Text
459 lines
13 KiB
Text
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/*
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* lum6Deuler implementation
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*
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* Copyright (C) Dorit Borrmann, Jan Elseberg, Andreas Nuechter, Kai Lingemann
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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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/**
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* @file
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* @brief The implementation of globally consistent scan matching algorithm
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*
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* @author Dorit Borrman. Institute of Computer Science, University of Osnabrueck, Germany.
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* @author Jan Elseberg. Institute of Computer Science, University of Osnabrueck, Germany.
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* @author Kai Lingemann. Institute of Computer Science, University of Osnabrueck, Germany.
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* @author Andreas Nuechter. Institute of Computer Science, University of Osnabrueck, Germany.
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*
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* A description of the algorithms implemented here can be found in the following paper
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* (ras2007.pdf):
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*
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* Dorit Borrmann, Jan Elseberg, Kai Lingemann, Andreas Nuechter, and Joachim Hertzberg.
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* Globally consistent 3D mapping with scan matching. Journal of Robotics and Autonomous
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* Systems (JRAS), Elsevier Science, Volume 56, Issue 2, ISSN 0921-8890, pages 130 - 142,
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* February 2008
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*/
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#ifdef _MSC_VER
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#if !defined _OPENMP && defined OPENMP
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#define _OPENMP
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#endif
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#endif
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#include "slam6d/lum6Deuler.h"
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#include "sparse/csparse.h"
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#include <cfloat>
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#include <fstream>
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using std::ofstream;
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using std::cerr;
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#include "slam6d/globals.icc"
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using namespace NEWMAT;
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/**
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* Constructor
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*
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* @param my_icp6Dminimizer Pointer to ICP minimization functor
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* @param mdm Maximum PtoP distance to which point pairs are collected for ICP
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* @param max_dist_match Maximum PtoP distance to which point pairs are collected for LUM
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* @param max_num_iterations Maximal number of iterations for ICP
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* @param quiet Suspesses all output to std out
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* @param meta Indicates if metascan matching has to be used
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* @param rnd Indicates if randomization has to be used
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* @param eP Extrapolate odometry?
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* @param anim Animate which frames?
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* @param epsilonICP Termination criterion for ICP
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* @param nns_method Specifies which NNS method to use
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* @param epsilonLUM Termination criterion for LUM
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*/
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lum6DEuler::lum6DEuler(icp6Dminimizer *my_icp6Dminimizer,
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double mdm, double max_dist_match,
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int max_num_iterations, bool quiet, bool meta, int rnd,
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bool eP, int anim, double epsilonICP, int nns_method, double epsilonLUM)
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: graphSlam6D(my_icp6Dminimizer,
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mdm, max_dist_match,
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max_num_iterations, quiet, meta, rnd,
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eP, anim, epsilonICP, nns_method, epsilonLUM)
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{ }
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/**
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* Destructor
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*/
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lum6DEuler::~lum6DEuler()
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{
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delete my_icp;
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}
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/**
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* This function calculates the inverse covariances Cij and the Vector Cij*Dij for
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* two scans by finding pointpairs.
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*
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* @param first pointer to the first scan of the link
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* @param second pointer to the second scan of the link
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* @param nns_method Specifies which NNS method to use
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* @param rnd shall we use randomization for computing the point pairs?
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* @param max_dist_match2 maximal distance allowed for point pairs
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* @param C pointer to the inverse of the covariance matrix Cij
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* @param CD pointer to the vector Cij*Dij
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*/
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void lum6DEuler::covarianceEuler(Scan *first, Scan *second,
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int nns_method, int rnd, double max_dist_match2,
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Matrix *C, ColumnVector *CD)
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{
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// x,y,z denote the coordinates of uk (Here averaged over ak and bk)
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// sx,sy,sz are the sums of their respective coordinates of uk over each paint pair
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// xpy,xpz,ypz are the sums over x*x + y*y ,x*x + z*z and y*y + z*z respectively over each point pair
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// xy,yz,xz are the sums over each respective multiplication
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// dx,dy,dz are the deltas in each coordinate of a point pair
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// ss is the estimation of the covariance of sensing error
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double x, y, z, sx, sy, sz, xy, yz, xz, ypz, xpz, xpy, dx, dy, dz, ss;
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// D is needed to calculate the estimation of the covariance s
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ColumnVector D(6);
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// Almost Cij*Dij
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ColumnVector MZ(6);
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// Almost the covarianve
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Matrix MM(6,6);
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// A set of point pairs
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vector <PtPair> uk;
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// A point pair
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Point ak, bk;
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// number of pairs in a set
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int m;
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#ifdef _OPENMP
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int thread_num = omp_get_thread_num();
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#else
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int thread_num = 0;
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#endif
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double dummy_centroid_m[3];
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double dummy_centroid_d[3];
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double dummy_sum;
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Scan::getPtPairs(&uk, first, second, thread_num,
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rnd, max_dist_match2, dummy_sum, dummy_centroid_m, dummy_centroid_d);
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m = uk.size();
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MZ = 0.0;
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MM = 0.0;
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sx = sy = sz = xy = yz = xz = ypz = xpz = xpy = ss = 0.0;
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if (m > 2) {
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// for each point pair
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for(int j = 0; j < m; j++){
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ak = uk[j].p1;
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bk = uk[j].p2;
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// Some temporary values
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x = (ak.x + bk.x)/2.0;
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y = (ak.y + bk.y)/2.0;
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z = (ak.z + bk.z)/2.0;
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dx = ak.x - bk.x;
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dy = ak.y - bk.y;
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dz = ak.z - bk.z;
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// Sum up all necessary values to construct MM
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sx += x;
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sy += y;
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sz += z;
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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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xy += x*y;
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xz += x*z;
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yz += y*z;
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// Sum up each part of MZ
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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) += -z * dy + y * dz;
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MZ(5) += -y * dx + x * dy;
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MZ(6) += z * dx - x * dz;
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}
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// Now construct the symmetrical matrix MM
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MM(1,1) = MM(2,2) = MM(3,3) = m;
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MM(4,4) = ypz;
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MM(5,5) = xpy;
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MM(6,6) = xpz;
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MM(1,5) = MM(5,1) = -sy;
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MM(1,6) = MM(6,1) = sz;
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MM(2,4) = MM(4,2) = -sz;
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MM(2,5) = MM(5,2) = sx;
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MM(3,4) = MM(4,3) = sy;
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MM(3,6) = MM(6,3) = -sx;
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MM(4,5) = MM(5,4) = -xz;
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MM(4,6) = MM(6,4) = -xy;
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MM(5,6) = MM(6,5) = -yz;
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// Calculate the pose difference estimation
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D = MM.i() * MZ ;
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// Again going through all point pairs to faster calculate s.
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// This cannot be done earlier as we need D, and therefore MM and MZ to do this
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for(int j = 0; j < m; j++){
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ak = uk[j].p1;
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bk = uk[j].p2;
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x = (ak.x + bk.x) / 2.0;
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y = (ak.y + bk.y) / 2.0;
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z = (ak.z + bk.z) / 2.0;
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ss += sqr(ak.x - bk.x - (D(1) - y * D(5) + z * D(6)))
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+ sqr(ak.y - bk.y - (D(2) - z * D(4) + x * D(5)))
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+ sqr(ak.z - bk.z - (D(3) + y * D(4) - x * D(6)));
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}
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ss = ss / (2*m - 3);
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// for dealing with numerical instabilities when identical point clouds are used in matching
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if (ss < 0.0000000000001) {
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ss = 0.0;
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MM(1,1) = MM(1,2) = MM(1,3) = 0.0;
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MM(2,1) = MM(2,2) = MM(2,3) = 0.0;
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MM(3,1) = MM(3,2) = MM(3,3) = 0.0;
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MZ(6) = MZ(1) = MZ(2) = 0.0;
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MZ(3) = MZ(4) = MZ(5) = 0.0;
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*C = 0;
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if(CD)
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*CD = 0;
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return;
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}
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ss = 1.0 / ss;
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if (CD) {
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*CD = MZ * ss;
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}
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*C = MM * ss;
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} else {
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// This case should not occur
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ss = 0.0;
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MM(1,1) = MM(1,2) = MM(1,3) = 0.0;
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MM(2,1) = MM(2,2) = MM(2,3) = 0.0;
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MM(3,1) = MM(3,2) = MM(3,3) = 0.0;
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MZ(6) = MZ(1) = MZ(2) = 0.0;
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MZ(3) = MZ(4) = MZ(5) = 0.0;
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*C = 0;
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if(CD)
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*CD = 0;
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cerr << "Error calculating covariance matrix" << endl;
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}
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}
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/**
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* A function to fill the linear system G X = B.
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*
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* @param gr the Graph is used to map the given covariances C and CD matrices to the correct link
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* @param CD A vector containing all covariances C multiplied with their respective estimations D
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* @param C A vector containing all covariances C of the pose difference estimations D
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* @param G The matrix G specifying the linear equation
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* @param B The vector B
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*/
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void lum6DEuler::FillGB3D(Graph *gr, GraphMatrix* G, ColumnVector* B,vector<Scan *> allScans )
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{
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#ifdef _OPENMP
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#pragma omp parallel for schedule(dynamic)
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#endif
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for(int i = 0; i < gr->getNrLinks(); i++){
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int a = gr->getLink(i,0) - 1;
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int b = gr->getLink(i,1) - 1;
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Scan *FirstScan = allScans[gr->getLink(i,0)];
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Scan *SecondScan = allScans[gr->getLink(i,1)];
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// cout << "i " << i << " a: " << a << " b: " << b << endl;
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Matrix Cab(6,6);
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ColumnVector CDab(6);
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covarianceEuler(FirstScan, SecondScan, nns_method, (int)my_icp->get_rnd(),
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(int)max_dist_match2_LUM, &Cab, &CDab);
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#pragma omp critical
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{
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if(a >= 0){
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B->Rows(a*6+1,a*6+6) += CDab;
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G->add(a, a, Cab);
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}
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if(b >= 0){
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B->Rows(b*6+1,b*6+6) -= CDab;
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G->add(b, b, Cab);
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}
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if(a >= 0 && b >= 0) {
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G->subtract(a, b, Cab);
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G->subtract(b, a, Cab);
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}
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}
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}
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// G->print();
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}
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/**
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* This function is used to match a set of laser scans with any minimally
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* connected Graph, using the globally consistent LUM-algorithm in 3D.
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*
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* @param gr Some Graph with no real subgraphs except for itself
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* @param allScans Contains all laser scans
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* @param nrIt The number of iterations the LUM-algorithm will run
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* @return Euclidian distance of all pose shifts
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*/
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double lum6DEuler::doGraphSlam6D(Graph gr, vector <Scan *> allScans, int nrIt)
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{
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#ifdef WRITE_GRAPH_NET
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// for debug only:
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static int d = 0;
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cout << "writing graph.dat ....................................." << endl;
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d++;
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string gfilename = "graph_" + to_string(d, 3) + ".net";
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ofstream out(gfilename.c_str());
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for (int i=0; i < gr.getNrLinks(); i++) {
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int from = gr.getLink(i,0);
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int to = gr.getLink(i,1);
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// shouldn't be necessary, just in case a (out of date) graph file is loaded:
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if (from < (int)allScans.size() && to < (int)allScans.size()) {
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out << allScans[from]->get_rPos()[0] << " "
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<< allScans[from]->get_rPos()[1] << " "
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<< allScans[from]->get_rPos()[2] << endl
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<< allScans[to ]->get_rPos()[0] << " "
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<< allScans[to ]->get_rPos()[1] << " "
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<< allScans[to ]->get_rPos()[2] << endl << endl;
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}
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}
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out.close();
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out.clear();
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#endif
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// the IdentityMatrix to transform some Scans with
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double id[16];
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M4identity(id);
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double ret = DBL_MAX;
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for(int iteration = 0;
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iteration < nrIt && ret > epsilonLUM;
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iteration++) {
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if (nrIt > 1) cout << "Iteration " << iteration << endl;
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// * Calculate X and CX from all Dij and Cij
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int n = (gr.getNrScans() - 1);
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// Construct the linear equation system..
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GraphMatrix *G = new GraphMatrix();
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ColumnVector B(6*n);
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B = 0.0;
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// ...fill G and B...
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FillGB3D(&gr, G, &B, allScans);
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// ...and solve it
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ColumnVector X = solveSparseCholesky(G, B);
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delete G;
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//cout << "X done!" << endl;
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double sum_position_diff = 0.0;
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// Start with second Scan
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int loop_end = gr.getNrScans();
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#ifdef _OPENMP
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#pragma omp parallel for reduction(+:sum_position_diff)
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#endif
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for(int i = 1; i < loop_end; i++){
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// Now update the Poses
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Matrix Ha = IdentityMatrix(6);
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double xa = allScans[i]->get_rPos()[0];
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double ya = allScans[i]->get_rPos()[1];
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double za = allScans[i]->get_rPos()[2];
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double tx = allScans[i]->get_rPosTheta()[0];
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double ty = allScans[i]->get_rPosTheta()[1];
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double ctx = cos(tx);
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double stx = sin(tx);
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double cty = cos(ty);
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double sty = sin(ty);
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// Fill Ha
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Ha.element(0,4) = -za*ctx+ya*stx;
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Ha.element(0,5) = ya*cty*ctx+za*stx*cty;
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Ha.element(1,3) = za;
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Ha.element(1,4) = -xa*stx;
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Ha.element(1,5) = -xa*ctx*cty+za*sty;
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Ha.element(2,3) = -ya;
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Ha.element(2,4) = xa*ctx;
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Ha.element(2,5) = -xa*cty*stx-ya*sty;
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Ha.element(3,5) = sty;
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Ha.element(4,4) = stx;
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Ha.element(4,5) = ctx*cty;
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Ha.element(5,4) = ctx;
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Ha.element(5,5) = -stx*cty;
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// Invert it
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Ha = Ha.i();
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// Get pose estimate
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ColumnVector Xtmp = X.Rows((i-1)*6+1,(i-1)*6+6);
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// Correct pose estimate
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ColumnVector result = Ha * Xtmp;
|
||
|
|
||
|
if(!quiet) {
|
||
|
cout << "Old pose estimate, Scan " << i << endl;
|
||
|
cout << "x: " << allScans[i]->get_rPos()[0]
|
||
|
<< " y: " << allScans[i]->get_rPos()[1]
|
||
|
<< " z: " << allScans[i]->get_rPos()[2]
|
||
|
<< " tx: " << allScans[i]->get_rPosTheta()[0]
|
||
|
<< " ty: " << allScans[i]->get_rPosTheta()[1]
|
||
|
<< " tz: " << allScans[i]->get_rPosTheta()[2]
|
||
|
<< endl;
|
||
|
}
|
||
|
|
||
|
double rPos[3];
|
||
|
double rPosTheta[3];
|
||
|
|
||
|
// calculate the updated Pose
|
||
|
for (int k = 0; k < 3; k++) {
|
||
|
rPos[k] = allScans[i]->get_rPos()[k] - result.element(k);
|
||
|
rPosTheta[k] = allScans[i]->get_rPosTheta()[k] - result.element(k+3);
|
||
|
}
|
||
|
|
||
|
// Update the Pose
|
||
|
if (i != gr.getNrScans() - 1) {
|
||
|
allScans[i]->transformToEuler(rPos, rPosTheta, Scan::LUM, 1);
|
||
|
} else {
|
||
|
allScans[i]->transformToEuler(rPos, rPosTheta, Scan::LUM, 2);
|
||
|
}
|
||
|
|
||
|
if(!quiet) {
|
||
|
cout << "x: " << allScans[i]->get_rPos()[0]
|
||
|
<< " y: " << allScans[i]->get_rPos()[1]
|
||
|
<< " z: " << allScans[i]->get_rPos()[2]
|
||
|
<< " tx: " << allScans[i]->get_rPosTheta()[0]
|
||
|
<< " ty: " << allScans[i]->get_rPosTheta()[1]
|
||
|
<< " tz: " << allScans[i]->get_rPosTheta()[2] << endl << endl;
|
||
|
}
|
||
|
|
||
|
double x[3];
|
||
|
x[0] = result.element(0);
|
||
|
x[1] = result.element(1);
|
||
|
x[2] = result.element(2);
|
||
|
sum_position_diff += Len(x);
|
||
|
}
|
||
|
cout << "Sum of Position differences = " << sum_position_diff << endl;
|
||
|
ret = (sum_position_diff / (double)gr.getNrScans());
|
||
|
}
|
||
|
|
||
|
return ret;
|
||
|
}
|
||
|
|
||
|
|