252 lines
7.9 KiB
C++
252 lines
7.9 KiB
C++
/******************************************************************************
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* Author: Laurent Kneip *
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* Contact: kneip.laurent@gmail.com *
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* License: Copyright (c) 2013 Laurent Kneip, ANU. All rights reserved. *
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* *
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* Redistribution and use in source and binary forms, with or without *
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* modification, are permitted provided that the following conditions *
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* are met: *
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* * Redistributions of source code must retain the above copyright *
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* notice, this list of conditions and the following disclaimer. *
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* * Redistributions in binary form must reproduce the above copyright *
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* notice, this list of conditions and the following disclaimer in the *
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* documentation and/or other materials provided with the distribution. *
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* * Neither the name of ANU nor the names of its contributors may be *
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* used to endorse or promote products derived from this software without *
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* specific prior written permission. *
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* *
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"*
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE *
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE *
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* ARE DISCLAIMED. IN NO EVENT SHALL ANU OR THE CONTRIBUTORS BE LIABLE *
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL *
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR *
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER *
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT *
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY *
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* OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF *
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* SUCH DAMAGE. *
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******************************************************************************/
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#include "random_generators.hpp"
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#include "time_measurement.hpp"
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#include <math.h>
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#include <iostream>
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using namespace Eigen;
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void
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opengv::initializeRandomSeed()
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{
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struct timeval tic;
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gettimeofday( &tic, 0 );
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srand ( tic.tv_usec );
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}
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Eigen::Vector3d
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opengv::generateRandomPoint( double maximumDepth, double minimumDepth )
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{
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Eigen::Vector3d cleanPoint;
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cleanPoint[0] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
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cleanPoint[1] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
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cleanPoint[2] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
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Eigen::Vector3d direction = cleanPoint / cleanPoint.norm();
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cleanPoint =
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(maximumDepth-minimumDepth) * cleanPoint + minimumDepth * direction;
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return cleanPoint;
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}
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Eigen::Vector3d
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opengv::generateRandomPointPlane()
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{
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Eigen::Vector3d cleanPoint;
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cleanPoint[0] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
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cleanPoint[1] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
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cleanPoint[2] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
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cleanPoint[0] = 6*cleanPoint[0];
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cleanPoint[1] = 6*cleanPoint[1];
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cleanPoint[2] = 2*cleanPoint[2]-6.0;
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return cleanPoint;
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}
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Eigen::Vector3d
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opengv::addNoise( double noiseLevel, Eigen::Vector3d cleanPoint )
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{
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//compute a vector in the normal plane (based on good conditioning)
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Eigen::Vector3d normalVector1;
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if(
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(fabs(cleanPoint[0]) > fabs(cleanPoint[1])) &&
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(fabs(cleanPoint[0]) > fabs(cleanPoint[2])) )
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{
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normalVector1[1] = 1.0;
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normalVector1[2] = 0.0;
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normalVector1[0] = -cleanPoint[1]/cleanPoint[0];
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}
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else
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{
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if(
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(fabs(cleanPoint[1]) > fabs(cleanPoint[0])) &&
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(fabs(cleanPoint[1]) > fabs(cleanPoint[2])) )
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{
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normalVector1[2] = 1.0;
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normalVector1[0] = 0.0;
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normalVector1[1] = -cleanPoint[2]/cleanPoint[1];
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}
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else
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{
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normalVector1[0] = 1.0;
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normalVector1[1] = 0.0;
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normalVector1[2] = -cleanPoint[0]/cleanPoint[2];
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}
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}
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normalVector1 = normalVector1 / normalVector1.norm();
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Eigen::Vector3d normalVector2 = cleanPoint.cross(normalVector1);
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double noiseX =
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noiseLevel * (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0 / 1.4142;
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double noiseY =
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noiseLevel * (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0 / 1.4142;
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Eigen::Vector3d noisyPoint =
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800 * cleanPoint + noiseX *normalVector1 + noiseY * normalVector2;
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noisyPoint = noisyPoint / noisyPoint.norm();
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return noisyPoint;
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}
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Eigen::Vector3d
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opengv::generateRandomTranslation( double maximumParallax )
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{
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Eigen::Vector3d translation;
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translation[0] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
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translation[1] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
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translation[2] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
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return maximumParallax * translation;
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}
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Eigen::Vector3d
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opengv::generateRandomDirectionTranslation( double parallax )
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{
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Eigen::Matrix3d rotation = generateRandomRotation();
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Eigen::Vector3d translation;
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translation << 1.0, 0.0, 0.0;
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translation = rotation * translation;
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translation = parallax * translation;
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return translation;
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}
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Eigen::Matrix3d
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opengv::generateRandomRotation( double maxAngle )
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{
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Eigen::Vector3d rpy;
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rpy[0] = ((double) rand())/ ((double) RAND_MAX);
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rpy[1] = ((double) rand())/ ((double) RAND_MAX);
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rpy[2] = ((double) rand())/ ((double) RAND_MAX);
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rpy[0] = maxAngle*2.0*(rpy[0]-0.5);
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rpy[1] = maxAngle*2.0*(rpy[1]-0.5);
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rpy[2] = maxAngle*2.0*(rpy[2]-0.5);
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Eigen::Matrix3d R1;
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R1(0,0) = 1.0;
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R1(0,1) = 0.0;
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R1(0,2) = 0.0;
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R1(1,0) = 0.0;
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R1(1,1) = cos(rpy[0]);
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R1(1,2) = -sin(rpy[0]);
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R1(2,0) = 0.0;
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R1(2,1) = -R1(1,2);
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R1(2,2) = R1(1,1);
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Eigen::Matrix3d R2;
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R2(0,0) = cos(rpy[1]);
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R2(0,1) = 0.0;
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R2(0,2) = sin(rpy[1]);
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R2(1,0) = 0.0;
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R2(1,1) = 1.0;
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R2(1,2) = 0.0;
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R2(2,0) = -R2(0,2);
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R2(2,1) = 0.0;
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R2(2,2) = R2(0,0);
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Eigen::Matrix3d R3;
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R3(0,0) = cos(rpy[2]);
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R3(0,1) = -sin(rpy[2]);
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R3(0,2) = 0.0;
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R3(1,0) =-R3(0,1);
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R3(1,1) = R3(0,0);
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R3(1,2) = 0.0;
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R3(2,0) = 0.0;
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R3(2,1) = 0.0;
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R3(2,2) = 1.0;
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Eigen::Matrix3d rotation = R3 * R2 * R1;
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rotation.col(0) = rotation.col(0) / rotation.col(0).norm();
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rotation.col(2) = rotation.col(0).cross(rotation.col(1));
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rotation.col(2) = rotation.col(2) / rotation.col(2).norm();
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rotation.col(1) = rotation.col(2).cross(rotation.col(0));
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rotation.col(1) = rotation.col(1) / rotation.col(1).norm();
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return rotation;
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}
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Eigen::Matrix3d
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opengv::generateRandomRotation()
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{
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Eigen::Vector3d rpy;
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rpy[0] = ((double) rand())/ ((double) RAND_MAX);
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rpy[1] = ((double) rand())/ ((double) RAND_MAX);
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rpy[2] = ((double) rand())/ ((double) RAND_MAX);
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rpy[0] = 2*M_PI*(rpy[0]-0.5);
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rpy[1] = M_PI*(rpy[1]-0.5);
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rpy[2] = 2*M_PI*(rpy[2]-0.5);
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Eigen::Matrix3d R1;
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R1(0,0) = 1.0;
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R1(0,1) = 0.0;
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R1(0,2) = 0.0;
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R1(1,0) = 0.0;
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R1(1,1) = cos(rpy[0]);
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R1(1,2) = -sin(rpy[0]);
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R1(2,0) = 0.0;
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R1(2,1) = -R1(1,2);
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R1(2,2) = R1(1,1);
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Eigen::Matrix3d R2;
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R2(0,0) = cos(rpy[1]);
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R2(0,1) = 0.0;
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R2(0,2) = sin(rpy[1]);
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R2(1,0) = 0.0;
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R2(1,1) = 1.0;
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R2(1,2) = 0.0;
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R2(2,0) = -R2(0,2);
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R2(2,1) = 0.0;
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R2(2,2) = R2(0,0);
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Eigen::Matrix3d R3;
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R3(0,0) = cos(rpy[2]);
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R3(0,1) = -sin(rpy[2]);
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R3(0,2) = 0.0;
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R3(1,0) =-R3(0,1);
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R3(1,1) = R3(0,0);
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R3(1,2) = 0.0;
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R3(2,0) = 0.0;
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R3(2,1) = 0.0;
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R3(2,2) = 1.0;
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Eigen::Matrix3d rotation = R3 * R2 * R1;
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rotation.col(0) = rotation.col(0) / rotation.col(0).norm();
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rotation.col(2) = rotation.col(0).cross(rotation.col(1));
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rotation.col(2) = rotation.col(2) / rotation.col(2).norm();
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rotation.col(1) = rotation.col(2).cross(rotation.col(0));
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rotation.col(1) = rotation.col(1) / rotation.col(1).norm();
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return rotation;
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}
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