FusionResults RegistrationRandom::getTransform(Eigen::MatrixXd guess) {
unsigned int s_nr_data = src->data.cols();//std::min(int(src->data.cols()),int(500000));
unsigned int d_nr_data = dst->data.cols();
refinement->allow_regularization = true;
//printf("s_nr_data: %i d_nr_data: %i\n",s_nr_data,d_nr_data);
int stepy = std::max(1,int(d_nr_data)/100000);
Eigen::Matrix<double, 3, Eigen::Dynamic> Y;
Eigen::Matrix<double, 3, Eigen::Dynamic> N;
Y.resize(Eigen::NoChange,d_nr_data/stepy);
N.resize(Eigen::NoChange,d_nr_data/stepy);
unsigned int ycols = Y.cols();
for(unsigned int i = 0; i < d_nr_data/stepy; i++) {
Y(0,i) = dst->data(0,i*stepy);
Y(1,i) = dst->data(1,i*stepy);
Y(2,i) = dst->data(2,i*stepy);
N(0,i) = dst->normals(0,i*stepy);
N(1,i) = dst->normals(1,i*stepy);
N(2,i) = dst->normals(2,i*stepy);
}
/// Build kd-tree
//nanoflann::KDTreeAdaptor<Eigen::Matrix3Xd, 3, nanoflann::metric_L2_Simple> kdtree(Y);
Eigen::VectorXd DST_INORMATION = Eigen::VectorXd::Zero(Y.cols());
for(unsigned int i = 0; i < d_nr_data/stepy; i++) {
DST_INORMATION(i) = dst->information(0,i*stepy);
}
double s_mean_x = 0;
double s_mean_y = 0;
double s_mean_z = 0;
for(unsigned int i = 0; i < s_nr_data; i++) {
s_mean_x += src->data(0,i);
s_mean_y += src->data(1,i);
s_mean_z += src->data(2,i);
}
s_mean_x /= double(s_nr_data);
s_mean_y /= double(s_nr_data);
s_mean_z /= double(s_nr_data);
double d_mean_x = 0;
double d_mean_y = 0;
double d_mean_z = 0;
for(unsigned int i = 0; i < d_nr_data; i++) {
d_mean_x += dst->data(0,i);
d_mean_y += dst->data(1,i);
d_mean_z += dst->data(2,i);
}
d_mean_x /= double(d_nr_data);
d_mean_y /= double(d_nr_data);
d_mean_z /= double(d_nr_data);
double stop = 0.00001;
Eigen::Affine3d Ymean = Eigen::Affine3d::Identity();
Ymean(0,3) = d_mean_x;
Ymean(1,3) = d_mean_y;
Ymean(2,3) = d_mean_z;
Eigen::Affine3d Xmean = Eigen::Affine3d::Identity();
Xmean(0,3) = s_mean_x;
Xmean(1,3) = s_mean_y;
Xmean(2,3) = s_mean_z;
std::vector< Eigen::Matrix<double, 3, Eigen::Dynamic> > all_X;
std::vector< Eigen::Affine3d > all_res;
std::vector< int > count_X;
std::vector< float > score_X;
std::vector< std::vector< Eigen::VectorXd > > all_starts;
int stepxsmall = std::max(1,int(s_nr_data)/250);
Eigen::VectorXd Wsmall (s_nr_data/stepxsmall);
for(unsigned int i = 0; i < s_nr_data/stepxsmall; i++) {
Wsmall(i) = src->information(0,i*stepxsmall);
}
double sumtime = 0;
double sumtimeSum = 0;
double sumtimeOK = 0;
int r = 0;
refinement->viewer = viewer;
refinement->visualizationLvl = 0;
//for(int r = 0; r < 1000; r++){
// while(true){
// double rx = 2.0*M_PI*0.0001*double(rand()%10000);
// double ry = 2.0*M_PI*0.0001*double(rand()%10000);
// double rz = 2.0*M_PI*0.0001*double(rand()%10000);
//double stop = 0;
double step = 0.1+2.0*M_PI/5;
for(double rx = 0; rx < 2.0*M_PI; rx += step) {
for(double ry = 0; ry < 2.0*M_PI; ry += step)
for(double rz = 0; rz < 2.0*M_PI; rz += step) {
printf("rx: %f ry: %f rz: %f\n",rx,ry,rz);
double start = getTime();
double meantime = 999999999999;
if(r != 0) {
meantime = sumtimeSum/double(sumtimeOK+1.0);
}
refinement->maxtime = std::min(0.5,3*meantime);
Eigen::VectorXd startparam = Eigen::VectorXd(3);
startparam(0) = rx;
startparam(1) = ry;
startparam(2) = rz;
Eigen::Affine3d randomrot = Eigen::Affine3d::Identity();
randomrot = Eigen::AngleAxisd(rx, Eigen::Vector3d::UnitX()) *
Eigen::AngleAxisd(ry, Eigen::Vector3d::UnitY()) *
Eigen::AngleAxisd(rz, Eigen::Vector3d::UnitZ());
Eigen::Affine3d current_guess = Ymean*randomrot*Xmean.inverse();//*Ymean;
refinement->target_points = 250;
FusionResults fr = refinement->getTransform(current_guess.matrix());
//fr.timeout = timestopped;
double stoptime = getTime();
sumtime += stoptime-start;
if(!fr.timeout) {
sumtimeSum += stoptime-start;
sumtimeOK++;
}
//printf("sumtime: %f\n",sumtime);
stop = fr.score;
Eigen::Matrix4d m = fr.guess;
current_guess = m;//fr.guess;
float m00 = current_guess(0,0);
float m01 = current_guess(0,1);
float m02 = current_guess(0,2);
float m03 = current_guess(0,3);
float m10 = current_guess(1,0);
float m11 = current_guess(1,1);
float m12 = current_guess(1,2);
float m13 = current_guess(1,3);
float m20 = current_guess(2,0);
float m21 = current_guess(2,1);
float m22 = current_guess(2,2);
float m23 = current_guess(2,3);
Eigen::Matrix<double, 3, Eigen::Dynamic> Xsmall;
Xsmall.resize(Eigen::NoChange,s_nr_data/stepxsmall);
for(unsigned int i = 0; i < s_nr_data/stepxsmall; i++) {
float x = src->data(0,i*stepxsmall);
float y = src->data(1,i*stepxsmall);
float z = src->data(2,i*stepxsmall);
Xsmall(0,i) = m00*x + m01*y + m02*z + m03;
Xsmall(1,i) = m10*x + m11*y + m12*z + m13;
Xsmall(2,i) = m20*x + m21*y + m22*z + m23;
}
bool exists = false;
for(unsigned int ax = 0; ax < all_X.size(); ax++) {
Eigen::Matrix<double, 3, Eigen::Dynamic> axX = all_X[ax];
Eigen::Affine3d axT = all_res[ax];
double diff = (Xsmall-axX).colwise().norm().mean();
if(diff < 20*stop) {
count_X[ax]++;
all_starts[ax].push_back(startparam);
int count = count_X[ax];
float score = score_X[ax];
std::vector< Eigen::VectorXd > starts = all_starts[ax];
for(int bx = ax-1; bx >= 0; bx--) {
if(count_X[bx] < count_X[bx+1]) {
count_X[bx+1] = count_X[bx];
score_X[bx+1] = score_X[bx];
all_X[bx+1] = all_X[bx];
all_starts[bx+1] = all_starts[bx];
all_res[bx+1] = all_res[bx];
all_X[bx] = axX;
count_X[bx] = count;
score_X[bx] = score;
all_starts[bx] = starts;
all_res[bx] = axT;
} else {
break;
}
}
exists = true;
break;
}
}
if(!exists) {
all_X.push_back(Xsmall);
count_X.push_back(1);
score_X.push_back(stop);
all_starts.push_back(std::vector< Eigen::VectorXd >());
all_starts.back().push_back(startparam);
all_res.push_back(current_guess);
}
r++;
}
}
FusionResults fr = FusionResults();
refinement->allow_regularization = false;
int tpbef = refinement->target_points;
refinement->target_points = 2000;
for(unsigned int ax = 0; ax < all_X.size(); ax++) {
Eigen::Matrix4d np = all_res[ax].matrix();
refinement->visualizationLvl = visualizationLvl;
if(ax < 25) {
double start = getTime();
FusionResults fr1 = refinement->getTransform(np);
double stop = getTime();
np = fr1.guess;
}
refinement->visualizationLvl = 0;
fr.candidates.push_back(np);
fr.counts.push_back(count_X[ax]);
fr.scores.push_back(1.0/score_X[ax]);
}
refinement->target_points = tpbef;
return fr;
}
FusionResults RegistrationSICP::getTransform(Eigen::MatrixXd guess){
Eigen::Matrix<double, 3, Eigen::Dynamic> X;// = src->data;
Eigen::Matrix<double, 3, Eigen::Dynamic> Y;// = dst->data;
Eigen::Matrix<double, 3, Eigen::Dynamic> N;// = dst->data;
float m00 = guess(0,0); float m01 = guess(0,1); float m02 = guess(0,2); float m03 = guess(0,3);
float m10 = guess(1,0); float m11 = guess(1,1); float m12 = guess(1,2); float m13 = guess(1,3);
float m20 = guess(2,0); float m21 = guess(2,1); float m22 = guess(2,2); float m23 = guess(2,3);
unsigned int s_nr_data = src->data.cols();
X.resize(Eigen::NoChange,s_nr_data);
for(unsigned int i = 0; i < s_nr_data; i++){
float x = src->data(0,i);
float y = src->data(1,i);
float z = src->data(2,i);
X(0,i) = m00*x + m01*y + m02*z + m03;
X(1,i) = m10*x + m11*y + m12*z + m13;
X(2,i) = m20*x + m21*y + m22*z + m23;
}
unsigned int d_nr_data = dst->data.cols();
Y.resize(Eigen::NoChange,d_nr_data);
N.resize(Eigen::NoChange,d_nr_data);
for(unsigned int i = 0; i < d_nr_data; i++){
Y(0,i) = dst->data(0,i);
Y(1,i) = dst->data(1,i);
Y(2,i) = dst->data(2,i);
N(0,i) = dst->normals(0,i);
N(1,i) = dst->normals(1,i);
N(2,i) = dst->normals(2,i);
}
SICP::Parameters pars;
pars.p = 0.5;
pars.max_icp = 5;
pars.max_inner = 1;
pars.max_outer = 30;
pars.stop = 0.0001;
pars.print_icpn = true;
Eigen::Matrix4d t = SICP::point_to_plane(X,Y,N,pars);
pcl::TransformationFromCorrespondences tfc;
for(unsigned int i = 0; i < s_nr_data; i++){
Eigen::Vector3f a (X(0,i), X(1,i), X(2,i));
Eigen::Vector3f b (src->data(0,i), src->data(1,i), src->data(2,i));
tfc.add(b,a);
}
Eigen::Matrix4d np = tfc.getTransformation().matrix().cast<double>();
return FusionResults(np,0);
/*
ICP::Parameters ipars;
ipars.f = ICP::PNORM;
ipars.p = 0.5;
//ipars.max_icp = 10;
//ipars.max_outer = 100;
//ipars.stop = 0.0001;
pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr scloud (new pcl::PointCloud<pcl::PointXYZRGBNormal>);
pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr dcloud (new pcl::PointCloud<pcl::PointXYZRGBNormal>);
scloud->points.clear();
for(unsigned int i = 0; i < s_nr_data; i++){
pcl::PointXYZRGBNormal p;p.x = s(0,i);p.y = s(1,i);p.z = s(2,i);p.b = 0;p.g = 255;p.r = 0;scloud->points.push_back(p);
}
dcloud->points.clear();
for(unsigned int i = 0; i < d_nr_data; i++){
pcl::PointXYZRGBNormal p;p.x = d(0,i);p.y = d(1,i);p.z = d(2,i);p.b = 0;p.g = 0;p.r = 255;dcloud->points.push_back(p);
}
viewer->addPointCloud<pcl::PointXYZRGBNormal> (scloud, pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBNormal>(scloud), "scloud");
viewer->addPointCloud<pcl::PointXYZRGBNormal> (dcloud, pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBNormal>(dcloud), "dcloud");
viewer->spin();
viewer->removeAllPointClouds();
for(int k = 0;true;k++){
Eigen::Matrix4d t = SICP::point_to_plane(s,d,dn,pars);
//Eigen::Matrix4d t = ICP::point_to_plane(s, d, dn,ipars);//, pars);
scloud->points.clear();
for(unsigned int i = 0; i < s_nr_data; i++){
pcl::PointXYZRGBNormal p;p.x = s(0,i);p.y = s(1,i);p.z = s(2,i);p.b = 0;p.g = 255;p.r = 0;scloud->points.push_back(p);
}
dcloud->points.clear();
for(unsigned int i = 0; i < d_nr_data; i++){
pcl::PointXYZRGBNormal p;p.x = d(0,i);p.y = d(1,i);p.z = d(2,i);p.b = 0;p.g = 0;p.r = 255;dcloud->points.push_back(p);
}
viewer->addPointCloud<pcl::PointXYZRGBNormal> (scloud, pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBNormal>(scloud), "scloud");
viewer->addPointCloud<pcl::PointXYZRGBNormal> (dcloud, pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBNormal>(dcloud), "dcloud");
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 4, "scloud");
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 4, "dcloud");
printf("%i\n",k);
if(k % 3000 == 0){ viewer->spin();}
else{ viewer->spinOnce();}
viewer->removeAllPointClouds();
}
//SICP::print();
*/
/*
float m00 = np(0,0); float m01 = np(0,1); float m02 = np(0,2); float m03 = np(0,3);
float m10 = np(1,0); float m11 = np(1,1); float m12 = np(1,2); float m13 = np(1,3);
float m20 = np(2,0); float m21 = np(2,1); float m22 = np(2,2); float m23 = np(2,3);
dcloud->points.clear();
for(unsigned int i = 0; i < s_nr_data; i++){
pcl::PointXYZRGBNormal p;
float x = s(0,i); float y = s(1,i); float z = s(2,i);
p.x = x+0.0001; p.y = y; p.z = z;
p.b = 255; p.g = 255; p.r = 0; dcloud->points.push_back(p);
}
scloud->points.clear();
for(unsigned int i = 0; i < s_nr_data; i++){
pcl::PointXYZRGBNormal p;
float x = src->data(0,i); float y = src->data(1,i); float z = src->data(2,i);
p.x = m00*x + m01*y + m02*z + m03; p.y = m10*x + m11*y + m12*z + m13; p.z = m20*x + m21*y + m22*z + m23;
p.b = 0; p.g = 0; p.r = 255; scloud->points.push_back(p);
}
viewer->addPointCloud<pcl::PointXYZRGBNormal> (scloud, pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBNormal>(scloud), "scloud");
viewer->addPointCloud<pcl::PointXYZRGBNormal> (dcloud, pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBNormal>(dcloud), "dcloud");
viewer->spin();
viewer->removeAllPointClouds();
exit(0);
return guess;
*/
}