void X3DConverter::startShape(const std::array<float, 12>& matrix) {
// Finding axis/angle from matrix using Eigen for its bullet proof implementation.
Eigen::Transform<float, 3, Eigen::Affine> t;
t.setIdentity();
for (unsigned int i = 0; i < 3; i++) {
for (unsigned int j = 0; j < 4; j++) {
t(i, j) = matrix[i+j*3];
}
}
Eigen::Matrix3f rotationMatrix;
Eigen::Matrix3f scaleMatrix;
t.computeRotationScaling(&rotationMatrix, &scaleMatrix);
Eigen::Quaternionf q;
Eigen::AngleAxisf aa;
q = rotationMatrix;
aa = q;
Eigen::Vector3f scale = scaleMatrix.diagonal();
Eigen::Vector3f translation = t.translation();
startNode(ID::Transform);
m_writers.back()->setSFVec3f(ID::translation, translation.x(), translation.y() , translation.z());
m_writers.back()->setSFRotation(ID::rotation, aa.axis().x(), aa.axis().y(), aa.axis().z(), aa.angle());
m_writers.back()->setSFVec3f(ID::scale, scale.x(), scale.y(), scale.z());
startNode(ID::Shape);
startNode(ID::Appearance);
startNode(ID::Material);
m_writers.back()->setSFColor<vector<float> >(ID::diffuseColor, RVMColorHelper::color(m_materials.back()));
endNode(ID::Material); // Material
endNode(ID::Appearance); // Appearance
}
bool StereoSensorProcessor::computeVariances(
const pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr pointCloud,
const Eigen::Matrix<double, 6, 6>& robotPoseCovariance,
Eigen::VectorXf& variances)
{
variances.resize(pointCloud->size());
// Projection vector (P).
const Eigen::RowVector3f projectionVector = Eigen::RowVector3f::UnitZ();
// Sensor Jacobian (J_s).
const Eigen::RowVector3f sensorJacobian = projectionVector * (rotationMapToBase_.transposed() * rotationBaseToSensor_.transposed()).toImplementation().cast<float>();
// Robot rotation covariance matrix (Sigma_q).
Eigen::Matrix3f rotationVariance = robotPoseCovariance.bottomRightCorner(3, 3).cast<float>();
// Preparations for#include <pcl/common/transforms.h> robot rotation Jacobian (J_q) to minimize computation for every point in point cloud.
const Eigen::Matrix3f C_BM_transpose = rotationMapToBase_.transposed().toImplementation().cast<float>();
const Eigen::RowVector3f P_mul_C_BM_transpose = projectionVector * C_BM_transpose;
const Eigen::Matrix3f C_SB_transpose = rotationBaseToSensor_.transposed().toImplementation().cast<float>();
const Eigen::Matrix3f B_r_BS_skew = kindr::getSkewMatrixFromVector(Eigen::Vector3f(translationBaseToSensorInBaseFrame_.toImplementation().cast<float>()));
for (unsigned int i = 0; i < pointCloud->size(); ++i)
{
// For every point in point cloud.
// Preparation.
pcl::PointXYZRGB point = pointCloud->points[i];
double disparity = sensorParameters_.at("depth_to_disparity_factor")/point.z;
Eigen::Vector3f pointVector(point.x, point.y, point.z); // S_r_SP
float heightVariance = 0.0; // sigma_p
// Measurement distance.
float measurementDistance = pointVector.norm();
// Compute sensor covariance matrix (Sigma_S) with sensor model.
float varianceNormal = pow(sensorParameters_.at("depth_to_disparity_factor") / pow(disparity, 2), 2)
* ((sensorParameters_.at("p_5") * disparity + sensorParameters_.at("p_2"))
* sqrt(pow(sensorParameters_.at("p_3") * disparity + sensorParameters_.at("p_4") - getJ(i), 2)
+ pow(240 - getI(i), 2)) + sensorParameters_.at("p_1"));
float varianceLateral = pow(sensorParameters_.at("lateral_factor") * measurementDistance, 2);
Eigen::Matrix3f sensorVariance = Eigen::Matrix3f::Zero();
sensorVariance.diagonal() << varianceLateral, varianceLateral, varianceNormal;
// Robot rotation Jacobian (J_q).
const Eigen::Matrix3f C_SB_transpose_times_S_r_SP_skew = kindr::getSkewMatrixFromVector(Eigen::Vector3f(C_SB_transpose * pointVector));
Eigen::RowVector3f rotationJacobian = P_mul_C_BM_transpose * (C_SB_transpose_times_S_r_SP_skew + B_r_BS_skew);
// Measurement variance for map (error propagation law).
heightVariance = rotationJacobian * rotationVariance * rotationJacobian.transpose();
heightVariance += sensorJacobian * sensorVariance * sensorJacobian.transpose();
// Copy to list.
variances(i) = heightVariance;
}
return true;
}
bool LaserSensorProcessor::computeVariances(
const pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr pointCloud,
const Eigen::Matrix<double, 6, 6>& robotPoseCovariance,
Eigen::VectorXf& variances)
{
variances.resize(pointCloud->size());
// Projection vector (P).
const Eigen::RowVector3f projectionVector = Eigen::RowVector3f::UnitZ();
// Sensor Jacobian (J_s).
const Eigen::RowVector3f sensorJacobian = projectionVector * (rotationMapToBase_.transposed() * rotationBaseToSensor_.transposed()).toImplementation().cast<float>();
// Robot rotation covariance matrix (Sigma_q).
Eigen::Matrix3f rotationVariance = robotPoseCovariance.bottomRightCorner(3, 3).cast<float>();
// Preparations for robot rotation Jacobian (J_q) to minimize computation for every point in point cloud.
const Eigen::Matrix3f C_BM_transpose = rotationMapToBase_.transposed().toImplementation().cast<float>();
const Eigen::RowVector3f P_mul_C_BM_transpose = projectionVector * C_BM_transpose;
const Eigen::Matrix3f C_SB_transpose = rotationBaseToSensor_.transposed().toImplementation().cast<float>();
const Eigen::Matrix3f B_r_BS_skew = kindr::linear_algebra::getSkewMatrixFromVector(Eigen::Vector3f(translationBaseToSensorInBaseFrame_.toImplementation().cast<float>()));
for (unsigned int i = 0; i < pointCloud->size(); ++i)
{
// For every point in point cloud.
// Preparation.
auto& point = pointCloud->points[i];
Eigen::Vector3f pointVector(point.x, point.y, point.z); // S_r_SP
float heightVariance = 0.0; // sigma_p
// Measurement distance.
float measurementDistance = pointVector.norm();
// Compute sensor covariance matrix (Sigma_S) with sensor model.
float varianceNormal = pow(sensorParameters_.at("min_radius"), 2);
float varianceLateral = pow(sensorParameters_.at("beam_constant") + sensorParameters_.at("beam_angle") * measurementDistance, 2);
Eigen::Matrix3f sensorVariance = Eigen::Matrix3f::Zero();
sensorVariance.diagonal() << varianceLateral, varianceLateral, varianceNormal;
// Robot rotation Jacobian (J_q).
const Eigen::Matrix3f C_SB_transpose_times_S_r_SP_skew = kindr::linear_algebra::getSkewMatrixFromVector(Eigen::Vector3f(C_SB_transpose * pointVector));
Eigen::RowVector3f rotationJacobian = P_mul_C_BM_transpose * (C_SB_transpose_times_S_r_SP_skew + B_r_BS_skew);
// Measurement variance for map (error propagation law).
heightVariance = rotationJacobian * rotationVariance * rotationJacobian.transpose();
heightVariance += sensorJacobian * sensorVariance * sensorJacobian.transpose();
// Copy to list.
variances(i) = heightVariance;
}
return true;
}
void PwnTracker::processFrame(const DepthImage& depthImage_,
const Eigen::Isometry3f& sensorOffset_,
const Eigen::Matrix3f& cameraMatrix_,
const Eigen::Isometry3f& initialGuess){
int r,c;
Eigen::Matrix3f scaledCameraMatrix = cameraMatrix_;
_currentCloudOffset = sensorOffset_;
_currentCameraMatrix = cameraMatrix_;
_currentDepthImage = depthImage_;
_currentCloud = makeCloud(r,c,scaledCameraMatrix,_currentCloudOffset,_currentDepthImage);
bool newFrame = false;
bool newRelation = false;
PwnTrackerFrame* currentTrackerFrame=0;
Eigen::Isometry3d relationMean;
if (_previousCloud){
_aligner->setCurrentSensorOffset(_currentCloudOffset);
_aligner->setCurrentFrame(_currentCloud);
_aligner->setReferenceSensorOffset(_previousCloudOffset);
_aligner->setReferenceFrame(_previousCloud);
_aligner->correspondenceFinder()->setImageSize(r,c);
PinholePointProjector* alprojector=dynamic_cast<PinholePointProjector*>(_aligner->projector());
alprojector->setCameraMatrix(scaledCameraMatrix);
alprojector->setImageSize(r,c);
Eigen::Isometry3f guess=_previousCloudTransform.inverse()*_globalT*initialGuess;
_aligner->setInitialGuess(guess);
double t0, t1;
t0 = g2o::get_time();
_aligner->align();
t1 = g2o::get_time();
std::cout << "Time: " << t1 - t0 << " seconds " << std::endl;
cerr << "inliers: " << _aligner->inliers() << endl;
// cerr << "chi2: " << _aligner->error() << endl;
// cerr << "chi2/inliers: " << _aligner->error()/_aligner->inliers() << endl;
// cerr << "initialGuess: " << t2v(guess).transpose() << endl;
// cerr << "transform : " << t2v(_aligner->T()).transpose() << endl;
if (_aligner->inliers()>0){
_globalT = _previousCloudTransform*_aligner->T();
//cerr << "TRANSFORM FOUND" << endl;
} else {
//cerr << "FAILURE" << endl;
_globalT = _globalT*guess;
}
convertScalar(relationMean, _aligner->T());
if (! (_counter%50) ) {
Eigen::Matrix3f R = _globalT.linear();
Eigen::Matrix3f E = R.transpose() * R;
E.diagonal().array() -= 1;
_globalT.linear() -= 0.5 * R * E;
}
_globalT.matrix().row(3) << 0,0,0,1;
newAlignmentCallbacks(_globalT, _aligner->T(), _aligner->inliers(), _aligner->error());
int maxInliers = r*c;
float inliersFraction = (float) _aligner->inliers()/(float) maxInliers;
cerr << "inliers/maxinliers/fraction: " << _aligner->inliers() << "/" << maxInliers << "/" << inliersFraction << endl;
if (inliersFraction<_newFrameInliersFraction){
newFrame = true;
newRelation = true;
// char filename[1024];
// sprintf (filename, "frame-%05d.pwn", _numKeyframes);
// frame->save(filename,1,true,_globalT);
_numKeyframes ++;
if (!_cache)
delete _previousCloud;
else{
_previousCloudHandle.release();
}
//_cache->unlock(_previousTrackerFrame);
// _aligner->setReferenceSensorOffset(_currentCloudOffset);
// _aligner->setReferenceFrame(_currentCloud);
_previousCloud = _currentCloud;
_previousCloudTransform = _globalT;
// cerr << "new frame added (" << _numKeyframes << ")" << endl;
// cerr << "inliers: " << _aligner->inliers() << endl;
// cerr << "maxInliers: " << maxInliers << endl;
// cerr << "chi2: " << _aligner->error() << endl;
// cerr << "chi2/inliers: " << _aligner->error()/_aligner->inliers() << endl;
// cerr << "initialGuess: " << t2v(guess).transpose() << endl;
// cerr << "transform : " << t2v(_aligner->T()).transpose() << endl;
// cerr << "globalTransform : " << t2v(_globalT).transpose() << endl;
} else { // previous frame but offset is small
delete _currentCloud;
_currentCloud = 0;
}
} else { // first frame
//ser.writeObject(*manager);
newFrame = true;
// _aligner->setReferenceSensorOffset(_currentCloudOffset);
// _aligner->setReferenceFrame(_currentCloud);
_previousCloud = _currentCloud;
_previousCloudTransform = _globalT;
_previousCloudOffset = _currentCloudOffset;
_numKeyframes ++;
/*Eigen::Isometry3f t = _globalT;
geometry_msgs::Point p;
p.x = t.translation().x();
p.y = t.translation().y();
p.z = t.translation().z();
m_odometry.points.push_back(p);
*/
}
_counter++;
if (newFrame) {
//cerr << "maing new frame, previous: " << _previousTrackerFrame << endl;
currentTrackerFrame = new PwnTrackerFrame(_manager);
//currentTrackerFrame->cloud.set(cloud);
currentTrackerFrame->cloud=_currentCloud;
currentTrackerFrame->sensorOffset = _currentCloudOffset;
boss_map::ImageBLOB* depthBLOB=new boss_map::ImageBLOB();
DepthImage_convert_32FC1_to_16UC1(depthBLOB->cvImage(),_currentDepthImage);
//depthImage.toCvMat(depthBLOB->cvImage());
depthBLOB->adjustFormat();
currentTrackerFrame->depthImage.set(depthBLOB);
boss_map::ImageBLOB* normalThumbnailBLOB=new boss_map::ImageBLOB();
boss_map::ImageBLOB* depthThumbnailBLOB=new boss_map::ImageBLOB();
makeThumbnails(depthThumbnailBLOB->cvImage(), normalThumbnailBLOB->cvImage(), _currentCloud,
_currentDepthImage.rows,
_currentDepthImage.cols,
_currentCloudOffset,
_currentCameraMatrix,
0.1);
normalThumbnailBLOB->adjustFormat();
depthThumbnailBLOB->adjustFormat();
currentTrackerFrame->depthThumbnail.set(depthThumbnailBLOB);
currentTrackerFrame->normalThumbnail.set(normalThumbnailBLOB);
currentTrackerFrame->cameraMatrix = _currentCameraMatrix;
currentTrackerFrame->imageRows = _currentDepthImage.rows;
currentTrackerFrame->imageCols = _currentDepthImage.cols;
Eigen::Isometry3d _iso;
convertScalar(_iso, _globalT);
currentTrackerFrame->setTransform(_iso);
convertScalar(currentTrackerFrame->sensorOffset, _currentCloudOffset);
currentTrackerFrame->setSeq(_seq++);
_manager->addNode(currentTrackerFrame);
//cerr << "AAAA" << endl;
if (_cache)
_currentCloudHandle=_cache->get(currentTrackerFrame);
//cerr << "BBBB" << endl;
//_cache->lock(currentTrackerFrame);
newFrameCallbacks(currentTrackerFrame);
currentTrackerFrame->depthThumbnail.set(0);
currentTrackerFrame->normalThumbnail.set(0);
currentTrackerFrame->depthImage.set(0);
}
if (newRelation) {
PwnTrackerRelation* rel = new PwnTrackerRelation(_manager);
rel->setTransform(relationMean);
Matrix6d omega;
convertScalar(omega, _aligner->omega());
omega.setIdentity();
omega *= 100;
rel->setInformationMatrix(omega);
rel->setTo(currentTrackerFrame);
rel->setFrom(_previousTrackerFrame);
//cerr << "saved relation" << _previousTrackerFrame << " " << currentTrackerFrame << endl;
_manager->addRelation(rel);
newRelationCallbacks(rel);
//ser.writeObject(*rel);
}
if (currentTrackerFrame) {
_previousTrackerFrame = currentTrackerFrame;
}
}
int main(int argc, char **argv){
#if 0
DOF6::TFLinkvf rot1,rot2;
Eigen::Matrix4f tf1 = build_random_tflink(rot1,3);
Eigen::Matrix4f tf2 = build_random_tflink(rot2,3);
DOF6::DOF6_Source<DOF6::TFLinkvf,DOF6::TFLinkvf,float> abc(rot1.makeShared(), rot2.makeShared());
abc.getRotation();
abc.getTranslation();
std::cout<<"tf1\n"<<tf1<<"\n";
std::cout<<"tf2\n"<<tf2<<"\n";
std::cout<<"tf1*2\n"<<tf1*tf2<<"\n";
std::cout<<"tf2*1\n"<<tf2*tf1<<"\n";
std::cout<<"tf1\n"<<rot1.getTransformation()<<"\n";
std::cout<<"tf2\n"<<rot2.getTransformation()<<"\n";
std::cout<<"tf1*2\n"<<(rot1+rot2).getTransformation()<<"\n";
rot1.check();
rot2.check();
return 0;
pcl::RotationFromCorrespondences rfc;
Eigen::Vector3f n, nn, v,n2,n3,z,y,tv;
float a = 0.1f;
z.fill(0);y.fill(0);
z(2)=1;y(1)=1;
nn.fill(0);
nn(0) = 1;
Eigen::AngleAxisf aa(a,nn);
nn.fill(100);
n.fill(0);
n(0) = 1;
n2.fill(0);
n2=n;
n2(1) = 0.2;
n3.fill(0);
n3=n;
n3(2) = 0.2;
n2.normalize();
n3.normalize();
tv.fill(1);
tv.normalize();
#if 0
#if 0
rfc.add(n,aa.toRotationMatrix()*n+nn,
1*n.cross(y),1*n.cross(z),
1*(aa.toRotationMatrix()*n).cross(y),1*(aa.toRotationMatrix()*n).cross(z),
1,1/sqrtf(3));
#else
rfc.add(n,aa.toRotationMatrix()*n,
0*n.cross(y),0*n.cross(z),
0*(aa.toRotationMatrix()*n).cross(y),0*(aa.toRotationMatrix()*n).cross(z),
1,0);
#endif
#if 1
rfc.add(n2,aa.toRotationMatrix()*n2+nn,
tv,tv,
tv,tv,
1,1);
#else
rfc.add(n2,aa.toRotationMatrix()*n2+nn,
1*n2.cross(y),1*n2.cross(z),
1*(aa.toRotationMatrix()*n2).cross(y),1*(aa.toRotationMatrix()*n2).cross(z),
1,1/sqrtf(3));
#endif
#else
float f=1;
Eigen::Vector3f cyl;
cyl.fill(1);
cyl(0)=1;
Eigen::Matrix3f cylM;
cylM.fill(0);
cylM.diagonal() = cyl;
rfc.add(n,aa.toRotationMatrix()*n,
f*n.cross(y),f*n.cross(z),
f*(aa.toRotationMatrix()*n).cross(y),f*(aa.toRotationMatrix()*n).cross(z),
1,0);
rfc.add(n2,aa.toRotationMatrix()*n2+nn,
1*n2.cross(y),1*n2.cross(z),
1*(aa.toRotationMatrix()*n2).cross(y),1*(aa.toRotationMatrix()*n2).cross(z),
1,1);
#endif
rfc.add(n3,aa.toRotationMatrix()*n3+nn,
//tv,tv,
//tv,tv,
n3.cross(y),n3.cross(z),
1*(aa.toRotationMatrix()*n3).cross(y),1*(aa.toRotationMatrix()*n3).cross(z),
1,1);
std::cout<<"comp matrix:\n"<<rfc.getTransformation()<<"\n\n";
std::cout<<"real matrix:\n"<<aa.toRotationMatrix()<<"\n\n";
return 0;
//rfc.covariance_.normalize();
rfc.covariance_ = (rfc.var_*rfc.covariance_.inverse().transpose()*rfc.covariance_);
std::cout<<"comp matrix: "<<rfc.getTransformation()<<"\n\n";
std::cout<<"real matrix: "<<aa.toRotationMatrix()*aa.toRotationMatrix()<<"\n\n";
return 0;
#endif
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
void rawCloudHandler(const sensor_msgs::PointCloud2ConstPtr& laserCloud)
{
//double timeScanCur = laserCloud->header.stamp.toSec();
ros::Time timestamp = laserCloud->header.stamp;
//bool returnBool;
pcl::fromROSMsg(*laserCloud, *laserCloudIn);
if (visualizationFlag)
{
viewer->removeAllPointClouds(0);
viewer->removeAllShapes(0);
}
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudRGB(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudLabeled(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZI>::Ptr cloudRaw(new pcl::PointCloud<pcl::PointXYZI>);
// std::vector<std::string> labelsC1;
// std::vector<std::string> labelsC2;
// pcl::PointCloud<SAFEFeatures>::Ptr featureCloudC1Acc(new pcl::PointCloud<SAFEFeatures>);
// pcl::PointCloud<SAFEFeatures>::Ptr featureCloudC2Acc(new pcl::PointCloud<SAFEFeatures>);
std::vector<std::string> labelsC1;
std::vector<std::string> labelsC2;
std::vector<std::string> labelsC3;
std::vector<std::string> labels;
pcl::PointCloud<AllFeatures>::Ptr featureCloudAcc(new pcl::PointCloud<AllFeatures>);
pcl::PointCloud<AllFeatures>::Ptr featureCloudC1Acc(new pcl::PointCloud<AllFeatures>);
pcl::PointCloud<AllFeatures>::Ptr featureCloudC2Acc(new pcl::PointCloud<AllFeatures>);
pcl::PointCloud<AllFeatures>::Ptr featureCloudC3Acc(new pcl::PointCloud<AllFeatures>);
std::vector<Eigen::Matrix3f> confMats;
pcl::PointCloud<pcl::PointXYZRGB>::Ptr classificationCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
std::vector<DatasetContainer> dataset;
std::string folder = "/home/mikkel/catkin_ws/src/trainingSet/";
pcl::copyPointCloud(*laserCloudIn, *cloudRGB);
pcl::copyPointCloud(*laserCloudIn, *cloudRaw);
// Single colored
if (visualizationFlag)
{
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> color1(cloudRGB, 0, 0, 255);
viewer->addPointCloud<pcl::PointXYZRGB>(cloudRGB,color1,"cloudRGB",viewP(RawCloud));
}
//viewer->addText ("RawCloud", 10, 10, "vPP text", viewP(RawCloud));
// Ground modeling
// pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudStat(new pcl::PointCloud<pcl::PointXYZRGB>);
// pcl::PointCloud<pcl::PointXYZ>::Ptr planeDistCloud(new pcl::PointCloud<pcl::PointXYZ>);
// pcl::copyPointCloud(*cloudRGB,*cloudStat);
// pcl::copyPointCloud(*cloudRGB,*planeDistCloud);
// Remove everything outside the two lines
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZI>);
//pcl::copyPointCloud(*cloudRaw, *cloud_filtered);
// Rotation
float thetaZ = theta*PI/180; // The angle of rotation in radians
Eigen::Affine3f transform_2 = Eigen::Affine3f::Identity();
transform_2.rotate (Eigen::AngleAxisf (thetaZ, Eigen::Vector3f::UnitZ()));
pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::transformPointCloud (*cloudRaw, *cloud_filtered, transform_2);
// Passthrough
pcl::PassThrough<pcl::PointXYZI> passX;
passX.setInputCloud (cloud_filtered);
passX.setFilterFieldName ("x");
passX.setFilterLimits (-0.5, 100.0);
//pass.setFilterLimitsNegative (true);
passX.filter (*cloud_filtered);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_filteredRGB(new pcl::PointCloud<pcl::PointXYZRGB>);
if (receivedHough)
{
pcl::PassThrough<pcl::PointXYZI> pass;
pass.setInputCloud (cloud_filtered);
pass.setFilterFieldName ("y");
pass.setFilterLimits (r1+WALL_CLEARANCE, r2-WALL_CLEARANCE);
//pass.setFilterLimitsNegative (true);
pass.filter (*cloud_filtered);
}
pcl::copyPointCloud(*cloud_filtered, *cloud_filteredRGB);
if (visualizationFlag)
{
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> color9(cloudRGB, 0, 0, 255);
viewer->addPointCloud<pcl::PointXYZRGB>(cloud_filteredRGB,color9,"LinesFiltered",viewP(LinesFiltered));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "LinesFiltered");
viewer->addText ("LinesFiltered", 10, 10, fontsize, 1, 1, 1, "LinesFiltered text", viewP(LinesFiltered));
}
// Grid Minimum
// pcl::PointCloud<pcl::PointXYZI>::Ptr gridCloud(new pcl::PointCloud<pcl::PointXYZI>);
// pcl::GridMinimum<pcl::PointXYZI> gridm(1.0); // Set grid resolution
// gridm.setInputCloud(cloud_filtered);
// gridm.filter(*gridCloud);
//*** Transform point cloud to adjust for a Ground Plane ***//
pcl::ModelCoefficients ground_coefficients;
pcl::PointIndices ground_indices;
pcl::SACSegmentation<pcl::PointXYZI> ground_finder;
ground_finder.setOptimizeCoefficients(true);
ground_finder.setModelType(pcl::SACMODEL_PLANE);
ground_finder.setMethodType(pcl::SAC_RANSAC);
ground_finder.setDistanceThreshold(0.15);
ground_finder.setInputCloud(cloud_filtered);
ground_finder.segment(ground_indices, ground_coefficients);
// Convert plane normal vector from type ModelCoefficients to Vector4f
Eigen::Vector3f np = Eigen::Vector3f(ground_coefficients.values[0],ground_coefficients.values[1],ground_coefficients.values[2]);
// Find rotation vector, u, and angle, theta
Eigen::Vector3f u = np.cross(Eigen::Vector3f(0,0,1));
u.normalize();
float theta = acos(np.dot(Eigen::Vector3f(0,0,1)));
// Construct transformation matrix (rotation matrix from axis and angle)
Eigen::Affine3f tf = Eigen::Affine3f::Identity();
float d = ground_coefficients.values[3];
tf.translation() << d*np[0], d*np[1], d*np[2];
tf.rotate (Eigen::AngleAxisf (theta, u));
// Execute the transformation
pcl::PointCloud<pcl::PointXYZI>::Ptr transformedCloud (new pcl::PointCloud<pcl::PointXYZI> ());
pcl::transformPointCloud(*cloud_filtered, *transformedCloud, tf);
// Compute statistical moments for least significant direction in neighborhood
#if (OLD_METHOD)
pcl::NeighborhoodFeatures<pcl::PointXYZI, AllFeatures> features;
pcl::PointCloud<AllFeatures>::Ptr featureCloud(new pcl::PointCloud<AllFeatures>);
features.setInputCloud(transformedCloud);
pcl::search::KdTree<pcl::PointXYZI>::Ptr search_tree5( new pcl::search::KdTree<pcl::PointXYZI>());
features.setSearchMethod(search_tree5);
//principalComponentsAnalysis.setRadiusSearch(0.6);
//features.setKSearch(30);
features.setRadiusSearch(0.01); // This value does not do anything! Look inside "NeighborgoodFeatures.h" to see adaptive radius calculation.
features.compute(*featureCloud);
// ros::Time tFeatureCalculation2 = ros::Time::now();
// ros::Duration tFeatureCalculation = tFeatureCalculation2-tPreprocessing2;
// if (executionTimes==true)
// ROS_INFO("Feature calculation time = %i",tFeatureCalculation.nsec);
visualizeFeature(*cloudRGB, *featureCloud, 0, viewP(GPDistMean), "GPDistMean");
visualizeFeature(*cloudRGB, *featureCloud, 1, viewP(GPDistMin), "GPDistMin");
visualizeFeature(*cloudRGB, *featureCloud, 2, viewP(GPDistPoint), "GPDistPoint");
visualizeFeature(*cloudRGB, *featureCloud, 3, viewP(GPDistVar), "GPDistVar");
visualizeFeature(*cloudRGB, *featureCloud, 4, viewP(PCA1), "PCA1"); // 4 = PCA1
visualizeFeature(*cloudRGB, *featureCloud, 5, viewP(PCA2), "PCA2"); // 3 = GPVar
visualizeFeature(*cloudRGB, *featureCloud, 6, viewP(PCA3), "PCA3"); // 0 = GPMean
visualizeFeature(*cloudRGB, *featureCloud, 7, viewP(PCANX), "PCANX");
visualizeFeature(*cloudRGB, *featureCloud, 8, viewP(PCANY), "PCANY");
visualizeFeature(*cloudRGB, *featureCloud, 9, viewP(PCANZ), "PCANZ"); // 9 = PCANZ
visualizeFeature(*cloudRGB, *featureCloud, 10, viewP(PointDist), "PointDist");
visualizeFeature(*cloudRGB, *featureCloud, 11, viewP(RSS), "RSS");
visualizeFeature(*cloudRGB, *featureCloud, 12, viewP(Reflectance), "Reflectance");
ROS_INFO("Computed all neighborhood features");
std::vector<float>* centroid = new std::vector<float>();
std::vector<float>* stds = new std::vector<float>();
//*** Testing ***//
if (training == false)
{
Eigen::Matrix3f confMat = Eigen::Matrix3f::Zero();
if (testdata==true)
{
*featureCloud = *featureCloudAcc;
}
pcl::copyPointCloud(*cloudRGB,*classificationCloud);
// Load feature normalization parameters
std::ifstream input_file((folder + "centroid").c_str());
std::istream_iterator<float> start(input_file), end;
std::vector<float> numbers(start, end);
std::copy(numbers.begin(), numbers.end(), std::back_inserter(*centroid));
std::ifstream input_file2((folder + "stds").c_str());
std::istream_iterator<float> start2(input_file2), end2;
std::vector<float> numbers2(start2, end2);
std::copy(numbers2.begin(), numbers2.end(), std::back_inserter(*stds));
// Normalize features
// if (pcl::io::savePCDFile ("testFeaturesNonNormalized.pcd", *featureCloud) != 0)
// return (false);
ros::Time tNormalization1 = ros::Time::now();
normalizeFeatures(*featureCloud,*featureCloud, ¢roid, &stds);
ros::Time tNormalization2 = ros::Time::now();
ros::Duration tNormalization = tNormalization2-tNormalization1;
if (executionTimes==true)
ROS_INFO("Normalization time = %f",(float)(tNormalization.nsec)/1000000);
pcl::PointIndices::Ptr unclassifiedIndices (new pcl::PointIndices ());
// if (pcl::io::savePCDFile ("testFeatures.pcd", *featureCloud) != 0)
// return (false);
CvSVM SVM;
//int dim = sizeof(featureCloud->points[0])/sizeof(float);
int dim = useFeatures.size();//sizeof(useFeatures)/sizeof(int);
SVM.load((folder + "svm").c_str());
//SVM.load((folder + "svm2014-11-06-11-22-59").c_str());
//SVM.load((folder + "svm2014-11-07-14-00-31").c_str());
//SVM.load((folder + "svm2014-11-07-13-16-28").c_str());
ros::Time tClassification1 = ros::Time::now();
//int nMissingLabels = 0;
for (size_t i=0;i<classificationCloud->points.size();i++)
{
float dataSVM[dim];
for (int d=0;d<dim;d++)
{
//dataSVM[d] = featureCloud->points[i].data[d];
dataSVM[d] = featureCloud->points[i].data[useFeatures[d]];
}
Mat labelsMat(1, dim, CV_32FC1, dataSVM);
float response = SVM.predict(labelsMat);
if (response == 1.0)
classificationCloud->points[i].b = 255;
else if (response == 2.0)
classificationCloud->points[i].g = 255;
else if (response == 3.0)
classificationCloud->points[i].r = 255;
else
ROS_INFO("Another class label = %f",response);
if (testdata==true)
{
int groundTruth;
if (labels[i].compare("ground") == 0)
groundTruth = 1;
else if (labels[i].compare("vegetation") == 0)
groundTruth = 2;
else if (labels[i].compare("object") == 0)
groundTruth = 3;
confMat(groundTruth-1,(int)response-1)++;
}
}
ros::Time tClassification2 = ros::Time::now();
ros::Duration tClassification = tClassification2-tClassification1;
if (executionTimes==true)
ROS_INFO("Classification time = %f",(float)(tClassification.nsec)/100000);
//ROS_INFO("Number of missing class labels = %i", nMissingLabels);
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC(classificationCloud);
viewer->addPointCloud<pcl::PointXYZRGB>(classificationCloud,colorC,"Classification",viewP(Classification));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "Classification");
viewer->addText ("Classification", 10, 10, fontsize, 1, 1, 1, "Classification text", viewP(Classification));
// Test set - calculate performance statistics
if (testdata == true)
{
std::cout << confMat << std::endl;
confMats.push_back(confMat);
if (true)//k==files.size()-1)
{
ofstream myfile;
string resultsFolder = "/home/mikkel/catkin_ws/src/Results/";
myfile.open ((resultsFolder + "run_number_here.txt").c_str());
// Distance threshold
myfile << "DistanceThreshold:" << distThr << ";\n";
// Neighborhood parameters
myfile << "Neighborhood:" << rmin << ";" << rmindist << ";" << rmax << ";" << rmaxdist << ";\n";
// Features
myfile << "Features:";
for (int d=0;d<dim;d++)
myfile << useFeatures[d] << ";";
myfile << "\n";
myfile << "ConfusionMatrix:";
for (size_t i=0;i<confMats.size();i++)
{
for (int r=0;r<confMats[i].rows();r++)
for (int c=0;c<confMats[i].cols();c++)
myfile << confMats[i](r,c) << ";";
myfile << "\n";
}
myfile << "Accuracy:" << confMat.diagonal().sum()/confMat.sum() << ";\n";
myfile.close();
}
}
featureCloudAcc->clear();
labels.clear();
#else
for (size_t i=0;i<transformedCloud->size();i++)
{
pcl::PointXYZI pTmp2 = transformedCloud->points[i];
pcl::PointXYZRGB pTmp;
pTmp.x = pTmp2.x;
pTmp.y = pTmp2.y;
pTmp.z = pTmp2.z;
pTmp.r = 255;
pTmp.g = 255;
if (pTmp.z > 0.1) // Object
{
classificationCloud->push_back(pTmp);
}
}
if (visualizationFlag)
{
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC(classificationCloud);
viewer->addPointCloud<pcl::PointXYZRGB>(classificationCloud,colorC,"Classification",viewP(Classification));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "Classification");
viewer->addText ("Classification", 10, 10, fontsize, 1, 1, 1, "Classification text", viewP(Classification));
}
#endif
// REGION GROWING
//ROS_INFO("region growing 5");
pcl::PointCloud <pcl::PointXYZRGB>::Ptr objectCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
//pcl::PointCloud <pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud <pcl::PointXYZRGB>);
for (size_t i=0;i<classificationCloud->size();i++)
{
pcl::PointXYZRGB pTmp = classificationCloud->points[i];
if ((pTmp.r == 255) || (pTmp.g == 255)) // Object
{
objectCloud->push_back(pTmp);
}
}
pcl::RegionGrowing<pcl::PointXYZRGB, pcl::Normal> reg;
reg.setInputCloud (objectCloud);
//reg.setIndices (indices);
pcl::search::Search<pcl::PointXYZRGB>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZRGB> > (new pcl::search::KdTree<pcl::PointXYZRGB>);
reg.setSearchMethod (tree);
//reg.setDistanceThreshold (0.0001f);
//reg.setResidualThreshold(0.01f);
//ROS_INFO("res = %f",reg.getSmoothnessThreshold());
//reg.setPointColorThreshold (6);
//reg.setRegionColorThreshold (5);
reg.setMinClusterSize (1);
reg.setResidualThreshold(min_cluster_distance);
reg.setCurvatureTestFlag(false);
// reg.setResidualTestFlag(true);
// reg.setNormalTestFlag(false);
// reg.setSmoothModeFlag(false);
std::vector <pcl::PointIndices> clusters;
reg.extract (clusters);
//ROS_INFO("clusters = %d", clusters.size());
//std::cout << "testefter" << std::endl;
pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = reg.getColoredCloud ();
pcl::PointCloud <pcl::PointXYZRGB>::Ptr clustersFilteredCloud(new pcl::PointCloud<pcl::PointXYZRGB>);
std::vector <pcl::PointIndices> clustersFiltered;
//pcl::PointXYZRGB min_pt, max_pt;
Eigen::Vector4f min_pt, max_pt;
obstacle_detection::boundingbox bbox;
obstacle_detection::boundingboxes bboxes;
bboxes.header.stamp = timestamp;
bboxes.header.frame_id = "/velodyne";
if (visualizationFlag)
viewer->removeAllShapes(viewP(SegmentsFiltered));
for (size_t i=0;i<clusters.size();i++)
{
//ROS_INFO("cluster size = %d",clusters[i].indices.size());
if (clusters[i].indices.size() > 100)
{
clustersFiltered.push_back(clusters[i]);
for (size_t pp=0;pp<clusters[i].indices.size();pp++)
{
clustersFilteredCloud->push_back(colored_cloud->points[clusters[i].indices[pp]]);
}
// Calculate bounding box
pcl::getMinMax3D(*colored_cloud, clusters[i], min_pt, max_pt);
bbox.start.x = min_pt[0];
bbox.start.y = min_pt[1];
bbox.start.z = min_pt[2];
bbox.width.x = max_pt[0]-min_pt[0];
bbox.width.y = max_pt[1]-min_pt[1];
bbox.width.z = max_pt[2]-min_pt[2];
bbox.header.stamp = timestamp;
bbox.header.frame_id = "/velodyne";
bboxes.boundingboxes.push_back(bbox);
if (visualizationFlag)
viewer->addCube (min_pt[0], max_pt[0], min_pt[1], max_pt[1], min_pt[2], max_pt[2], 1.0f, 1.0f, 1.0f, (boost::format("%04d") % i).str().c_str(), viewP(SegmentsFiltered));
}
}
pubBBoxesPointer->publish(bboxes);
//
// //pcl::visualization::CloudViewer viewer ("Cluster viewer");
// //viewer.showCloud (colored_cloud);
//
if (visualizationFlag)
{
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC2(colored_cloud);
viewer->addPointCloud<pcl::PointXYZRGB>(colored_cloud,colorC2,"Segments",viewP(Segments));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "Segments");
viewer->addText ("Segments", 10, 10, fontsize, 1, 1, 1, "Segments text", viewP(Segments));
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorC3(clustersFilteredCloud);
viewer->addPointCloud<pcl::PointXYZRGB>(clustersFilteredCloud,colorC3,"SegmentsFiltered",viewP(SegmentsFiltered));
viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "SegmentsFiltered");
viewer->addText ("Segments", 10, 10, fontsize, 1, 1, 1, "SegmentsFiltered text", viewP(SegmentsFiltered));
}
//
//
// // BOUNDING BOXES
// viewer->addCube (min_pt.x, max_pt.x, min_pt.y, max_pt.y, min_pt.z, max_pt.z);
// std_msgs::Float64 testF;
// //testF.data = 10;
// testF.data = 10;
//
// Compute principal directions
// Eigen::Vector4f pcaCentroid;
// pcl::compute3DCentroid(*clustersFilteredCloud, pcaCentroid);
// Eigen::Matrix3f covariance;
// computeCovarianceMatrixNormalized(*clustersFilteredCloud, pcaCentroid, covariance);
// Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eigen_solver(covariance, Eigen::ComputeEigenvectors);
// Eigen::Matrix3f eigenVectorsPCA = eigen_solver.eigenvectors();
// eigenVectorsPCA.col(2) = eigenVectorsPCA.col(0).cross(eigenVectorsPCA.col(1));
//
// // Transform the original cloud to the origin where the principal components correspond to the axes.
// Eigen::Matrix4f projectionTransform(Eigen::Matrix4f::Identity());
// projectionTransform.block<3,3>(0,0) = eigenVectorsPCA.transpose();
// projectionTransform.block<3,1>(0,3) = -1.f * (projectionTransform.block<3,3>(0,0) * pcaCentroid.head<3>());
// pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudPointsProjected (new pcl::PointCloud<pcl::PointXYZRGB>);
// pcl::transformPointCloud(*clustersFilteredCloud, *cloudPointsProjected, projectionTransform);
// // Get the minimum and maximum points of the transformed cloud.
// pcl::PointXYZRGB minPoint, maxPoint;
// pcl::getMinMax3D(*cloudPointsProjected, minPoint, maxPoint);
// const Eigen::Vector3f meanDiagonal = 0.5f*(maxPoint.getVector3fMap() + minPoint.getVector3fMap());
//
// // Final transform
// const Eigen::Quaternionf bboxQuaternion(eigenVectorsPCA); //Quaternions are a way to do rotations https://www.youtube.com/watch?v=mHVwd8gYLnI
// const Eigen::Vector3f bboxTransform = eigenVectorsPCA * meanDiagonal + pcaCentroid.head<3>();
// viewer->addCube(bboxTransform, bboxQuaternion, maxPoint.x - minPoint.x, maxPoint.y - minPoint.y, maxPoint.z - minPoint.z, "bbox");
//pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> colorTTF1(featureCloudTest, 0, 255, 0);
// pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> colorTTF1(featureCloudTest);
// PCL_INFO("featureCloudTest size = %i",featureCloudTest->points.size());
// pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> colorTTF2(featureCloudTrain, 255, 0, 0);
// viewer->addPointCloud<pcl::PointXYZRGB>(featureCloudTest,colorTTF1,"TestTrainFeatures1",viewP(TestTrainFeatures));
// viewer->addPointCloud<pcl::PointXYZRGB>(featureCloudTrain,colorTTF2,"TestTrainFeatures2",viewP(TestTrainFeatures));
// viewer->addText ("TestTrainFeatures", 10, 10, "TestTrainFeatures text", viewP(TestTrainFeatures));
// sensor_msgs::PointCloud2 processedCloud;
// pcl::toROSMsg(*classificationCloud, processedCloud);
// processedCloud.header.stamp = ros::Time::now();//processedCloud->header.stamp;
// processedCloud.header.frame_id = "/velodyne";
// pubProcessedPointCloudPointer->publish(processedCloud);
//
// pcl::PointCloud<pcl::PointXYZRGB>::Ptr groundCloud2D(new pcl::PointCloud<pcl::PointXYZRGB>);
// pcl::PointCloud<pcl::PointXYZRGB>::Ptr objectCloud2D(new pcl::PointCloud<pcl::PointXYZRGB>);
//
// for (size_t i=0;i<classificationCloud->size();i++)
// {
// pcl::PointXYZRGB pTmp = classificationCloud->points[i];
// pTmp.z = 0; // 3D -> 2D
// if ((pTmp.r == 255) || (pTmp.g == 255)) // Object
// {
// objectCloud2D->push_back(pTmp);
// }
// else if (pTmp.b == 255) // Object
// {
// groundCloud2D->push_back(pTmp);
// }
// }
//
// //std::vector<int> indexVector;
// unsigned int leafNodeCounter = 0;
// unsigned int voxelDensity = 0;
// unsigned int totalPoints = 0;
// //int occupancyW = OCCUPANCY_WIDTH/OCCUPANCY_GRID_RESOLUTION;
// //int occupancyH = OCCUPANCY_DEPTH/OCCUPANCY_GRID_RESOLUTION;
// std::vector<unsigned int> ocGroundGrid(OCCUPANCY_WIDTH*OCCUPANCY_DEPTH, 0);
// std::vector<unsigned int> ocObjectGrid(OCCUPANCY_WIDTH*OCCUPANCY_DEPTH, 0);
// std::vector<signed char> ocGridFinal(OCCUPANCY_WIDTH*OCCUPANCY_DEPTH,-1);
//
// // Ground grid
// pcl::octree::OctreePointCloudDensity< pcl::PointXYZRGB > octreeGround (OCCUPANCY_GRID_RESOLUTION);
// octreeGround.setInputCloud (groundCloud2D);
// pcl::PointXYZ bot(-OCCUPANCY_DEPTH_M/2,-OCCUPANCY_WIDTH_M/2,-0.5);
// pcl::PointXYZ top(OCCUPANCY_DEPTH_M/2,OCCUPANCY_WIDTH_M/2,0.5);
// octreeGround.defineBoundingBox(bot.x, bot.y, bot.z, top.x, top.y, top.z); // I have already calculated the BBox of my point cloud
// octreeGround.addPointsFromInputCloud ();
// pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it1;
// pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it1_end = octreeGround.leaf_end();
// int minx = 1000;
// int miny = 1000;
// int maxx = -1000;
// int maxy = -1000;
// for (it1 = octreeGround.leaf_begin(); it1 != it1_end; ++it1)
// {
// pcl::octree::OctreePointCloudDensityContainer& container = it1.getLeafContainer();
// voxelDensity = container.getPointCounter();
// Eigen::Vector3f min_pt, max_pt;
// octreeGround.getVoxelBounds (it1, min_pt, max_pt);
// //ROS_INFO("x = %f, y = %f, z = %f, x2 = %f, y2 = %f, z2 = %f",min_pt[0],min_pt[1],min_pt[2],max_pt[0],max_pt[1],max_pt[2]);
// totalPoints += voxelDensity;
// if (min_pt[0] < minx)
// minx = min_pt[0];
// if (min_pt[0] > maxx)
// maxx = min_pt[0];
// if (min_pt[1] < miny)
// miny = min_pt[1];
// if (min_pt[1] > maxy)
// maxy = min_pt[1];
//
// int r = OCCUPANCY_DEPTH-(min_pt[0]+OCCUPANCY_DEPTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
// int c = OCCUPANCY_WIDTH-(min_pt[1]+OCCUPANCY_WIDTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
//
// // if ((r == 51) || (c==51))
// // ROS_INFO("r = %i,c = %i",r,c);
//
// //if (((int)min_pt[0] == -1) || ((int)min_pt[1]==-1))
// //ROS_INFO("min_pt[0] = %f, min_pt[1]=%f",min_pt[0],min_pt[1]);
// if (!((r < 0) || (c < 0) || (r > OCCUPANCY_DEPTH-1) || (c > OCCUPANCY_WIDTH-1)))
// ocGroundGrid[r+OCCUPANCY_DEPTH*c] = voxelDensity;
// //ROS_INFO("density = %i, r = %i, c=%i",voxelDensity, r, c);
// //ROS_INFO("x (%f) -> r (%i), y (%f) -> c (%i)",min_pt[0],r,min_pt[1],c);
// leafNodeCounter++;
// }
//
//
// // Object grid
// pcl::octree::OctreePointCloudDensity< pcl::PointXYZRGB > octreeObject (OCCUPANCY_GRID_RESOLUTION);
// octreeObject.setInputCloud (objectCloud2D);
// octreeObject.defineBoundingBox(bot.x, bot.y, bot.z, top.x, top.y, top.z); // I have already calculated the BBox of my point cloud
// octreeObject.addPointsFromInputCloud ();
// pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it2;
// pcl::octree::OctreePointCloudDensity<pcl::PointXYZRGB>::LeafNodeIterator it2_end = octreeObject.leaf_end();
// minx = 1000;
// miny = 1000;
// maxx = -1000;
// maxy = -1000;
// leafNodeCounter = 0;
// voxelDensity = 0;
// totalPoints = 0;
// int t1 = 0;
// int t2 = 0;
// int t3 = 0;
// for (it2 = octreeObject.leaf_begin(); it2 != it2_end; ++it2)
// {
// pcl::octree::OctreePointCloudDensityContainer& container = it2.getLeafContainer();
// voxelDensity = container.getPointCounter();
// Eigen::Vector3f min_pt, max_pt;
// octreeObject.getVoxelBounds (it2, min_pt, max_pt);
// //ROS_INFO("x = %f, y = %f, z = %f, x2 = %f, y2 = %f, z2 = %f",min_pt[0],min_pt[1],min_pt[2],max_pt[0],max_pt[1],max_pt[2]);
// totalPoints += voxelDensity;
// if (min_pt[0] < minx)
// minx = min_pt[0];
// if (min_pt[0] > maxx)
// maxx = min_pt[0];
// if (min_pt[1] < miny)
// miny = min_pt[1];
// if (min_pt[1] > maxy)
// maxy = min_pt[1];
//
// int r = OCCUPANCY_DEPTH-(min_pt[0]+OCCUPANCY_DEPTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
// int c = OCCUPANCY_WIDTH-(min_pt[1]+OCCUPANCY_WIDTH_M/2)/OCCUPANCY_GRID_RESOLUTION;
// if (!((r < 0) || (c < 0) || (r > OCCUPANCY_DEPTH-1) || (c > OCCUPANCY_WIDTH-1)))
// ocObjectGrid[r+OCCUPANCY_DEPTH*c] = voxelDensity;
// //ROS_INFO("object points = %i -> %i: x (%f) -> r (%i), y (%f) -> c (%i)",voxelDensity,ocGridFinal[r+OCCUPANCY_DEPTH*c],min_pt[0],r,min_pt[1],c);
// //ocGrid[r+OCCUPANCY_DEPTH*c] = voxelDensity;
// //ROS_INFO("density = %i, r = %i, c=%i",voxelDensity, r, c);
//
// leafNodeCounter++;
// }
//
// for (int r=0;r<OCCUPANCY_DEPTH;r++)
// {
// for (int c=0;c<OCCUPANCY_WIDTH;c++)
// {
// if ((ocGroundGrid[r+OCCUPANCY_DEPTH*c] == 0) && (ocObjectGrid[r+OCCUPANCY_DEPTH*c] <= 1))
// {
// ocGridFinal[r+OCCUPANCY_DEPTH*c] = -1.0; // 0.5 default likelihood
// t1++;
// }
// else if ((ocObjectGrid[r+OCCUPANCY_DEPTH*c] == 0))// && (ocGroundGrid[r+OCCUPANCY_DEPTH*c] > 0))
// {
// ocGridFinal[r+OCCUPANCY_DEPTH*c] = OCCUPANCY_MIN_PROB;
// t2++;
// }
// else
// {
// ocGridFinal[r+OCCUPANCY_DEPTH*c] = 50+(OCCUPANCY_MAX_PROB-50)*ocObjectGrid[r+OCCUPANCY_DEPTH*c]/(ocObjectGrid[r+OCCUPANCY_DEPTH*c]+ocGroundGrid[r+OCCUPANCY_DEPTH*c]);
// t3++;
// }
// }
// }
//
// nav_msgs::OccupancyGrid occupancyGrid;
// occupancyGrid.info.resolution = OCCUPANCY_GRID_RESOLUTION;
// occupancyGrid.header.stamp = timestamp;//ros::Time::now();
// occupancyGrid.header.frame_id = "/velodyne";
// occupancyGrid.info.width = OCCUPANCY_WIDTH;
// occupancyGrid.info.height = OCCUPANCY_DEPTH;
// //geometry_msgs::Pose lidarPose;
// geometry_msgs::Point lidarPoint;
// lidarPoint.x = 0;
// lidarPoint.y = 0;
// lidarPoint.z = 0;
// geometry_msgs::Quaternion lidarQuaternion;
// lidarQuaternion.w = 1;
// lidarQuaternion.x = 0;
// lidarQuaternion.y = 0;
// lidarQuaternion.z = 0;
// occupancyGrid.info.origin.position = lidarPoint;
// occupancyGrid.info.origin.orientation = lidarQuaternion;
// occupancyGrid.data = ocGridFinal;
//
// //ROS_INFO("total points = %i, total leaves = %i",totalPoints,leafNodeCounter);
// //ROS_INFO("minx = %i, maxx = %i, miny = %i, maxy = %i",minx,maxx,miny,maxy);
// //ROS_INFO("t1 = %i, t2 = %i, t3 = %i",t1,t2,t3);
//
//
// pubOccupancyGridPointer->publish(occupancyGrid);
// int l = 0;
// while (*++itLeafs)
// {
// //Iteratively explore only the leaf nodes..
// std::vector<PointXYZ> points;
// itLeafs->getData(points);
// l++;
// }
//
// ROS_INFO("l = %i",l);
//pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (OCCUPANCY_GRID_RESOLUTION);
// sensor_msgs::PointCloud2::Ptr cloud_filtered (new sensor_msgs::PointCloud2 ());
// pcl::VoxelGrid<sensor_msgs::PointCloud2> sor;
// sor.setInputCloud (processedCloud);
// sor.setLeafSize (0.01f, 0.01f, 0.01f);
// sor.filter (*cloud_filtered);
if (n==0)
{
//viewer->setCameraPosition(-20,0,10,1,0,2,1,0,2,v1);
//viewer->setCameraPosition(-20,0,10,1,0,2,1,0,2,v2);
//viewer->loadCameraParameters("pcl_video.cam");
}
#if (OLD_METHOD)
}
#endif
if (visualizationFlag)
viewer->spinOnce();
// Save screenshot
// std::stringstream tmp;
// tmp << n;
// viewer->saveScreenshot("/home/mikkel/images/" + (boost::format("%04d") % n).str() + ".png");
// n++;
}
void TwoDepthImageAlignerNode::processNode(MapNode* node_){
cerr << "START ITERATION" << endl;
std::vector<Serializable*> crearedObjects;
SensingFrameNode* sensingFrame = dynamic_cast<SensingFrameNode*>(node_);
if (! sensingFrame)
return;
PinholeImageData* image = dynamic_cast<PinholeImageData*>(sensingFrame->sensorData(_topic));
if (! image)
return;
cerr << "got image" << endl;
Eigen::Isometry3d _sensorOffset = _config->sensorOffset(image->baseSensor());
// cerr << "sensorOffset: " << endl;
// cerr << _sensorOffset.matrix() << endl;
Eigen::Isometry3f sensorOffset;
convertScalar(sensorOffset,_sensorOffset);
sensorOffset.matrix().row(3) << 0,0,0,1;
Eigen::Matrix3d _cameraMatrix = image->cameraMatrix();
ImageBLOB* blob = image->imageBlob().get();
DepthImage depthImage;
depthImage.fromCvMat(blob->cvImage());
int r=depthImage.rows();
int c=depthImage.cols();
DepthImage scaledImage;
Eigen::Matrix3f cameraMatrix;
convertScalar(cameraMatrix,_cameraMatrix);
//computeScaledParameters(r,c,cameraMatrix,_scale);
PinholePointProjector* projector=dynamic_cast<PinholePointProjector*>(_converter->projector());
cameraMatrix(2,2)=1;
projector->setCameraMatrix(cameraMatrix);
projector->setImageSize(depthImage.rows(), depthImage.cols());
projector->scale(1.0/_scale);
DepthImage::scale(scaledImage,depthImage,_scale);
pwn::Frame* frame = new pwn::Frame;
_converter->compute(*frame,scaledImage, sensorOffset);
MapNodeBinaryRelation* odom=0;
std::vector<MapNode*> oneNode(1);
oneNode[0]=sensingFrame;
MapNodeUnaryRelation* imu = extractRelation<MapNodeUnaryRelation>(oneNode);
if (_previousFrame){
_aligner->setReferenceSensorOffset(_aligner->currentSensorOffset());
_aligner->setCurrentSensorOffset(sensorOffset);
_aligner->setReferenceFrame(_previousFrame);
_aligner->setCurrentFrame(frame);
PinholePointProjector* projector=(PinholePointProjector*)(_aligner->projector());
projector->setCameraMatrix(cameraMatrix);
projector->setImageSize(depthImage.rows(), depthImage.cols());
projector->scale(1.0/_scale);
_aligner->correspondenceFinder()->setImageSize(projector->imageRows(), projector->imageCols());
/*
cerr << "correspondenceFinder: " << r << " " << c << endl;
cerr << "sensorOffset" << endl;
cerr <<_aligner->currentSensorOffset().matrix() << endl;
cerr <<_aligner->referenceSensorOffset().matrix() << endl;
cerr << "cameraMatrix" << endl;
cerr << projector->cameraMatrix() << endl;
*/
std::vector<MapNode*> twoNodes(2);
twoNodes[0]=_previousSensingFrameNode;
twoNodes[1]=sensingFrame;
odom = extractRelation<MapNodeBinaryRelation>(twoNodes);
cerr << "odom:" << odom << " imu:" << imu << endl;
Eigen::Isometry3f guess= Eigen::Isometry3f::Identity();
_aligner->clearPriors();
if (odom){
Eigen::Isometry3f mean;
Eigen::Matrix<float,6,6> info;
convertScalar(mean,odom->transform());
mean.matrix().row(3) << 0,0,0,1;
convertScalar(info,odom->informationMatrix());
//cerr << "odom: " << t2v(mean).transpose() << endl;
_aligner->addRelativePrior(mean,info);
//guess = mean;
}
if (imu){
Eigen::Isometry3f mean;
Eigen::Matrix<float,6,6> info;
convertScalar(mean,imu->transform());
convertScalar(info,imu->informationMatrix());
mean.matrix().row(3) << 0,0,0,1;
//cerr << "imu: " << t2v(mean).transpose() << endl;
_aligner->addAbsolutePrior(_globalT,mean,info);
}
_aligner->setInitialGuess(guess);
cerr << "Frames: " << _previousFrame << " " << frame << endl;
// projector->setCameraMatrix(cameraMatrix);
// projector->setTransform(Eigen::Isometry3f::Identity());
// Eigen::MatrixXi debugIndices(r,c);
// DepthImage debugImage(r,c);
// projector->project(debugIndices, debugImage, frame->points());
_aligner->align();
// sprintf(buf, "img-dbg-%05d.pgm",j);
// debugImage.save(buf);
//sprintf(buf, "img-ref-%05d.pgm",j);
//_aligner->correspondenceFinder()->referenceDepthImage().save(buf);
//sprintf(buf, "img-cur-%05d.pgm",j);
//_aligner->correspondenceFinder()->currentDepthImage().save(buf);
cerr << "inliers: " << _aligner->inliers() << endl;
cerr << "chi2: " << _aligner->error() << endl;
cerr << "chi2/inliers: " << _aligner->error()/_aligner->inliers() << endl;
cerr << "initialGuess: " << t2v(guess).transpose() << endl;
cerr << "transform : " << t2v(_aligner->T()).transpose() << endl;
if (_aligner->inliers()>-1){
_globalT = _globalT*_aligner->T();
cerr << "TRANSFORM FOUND" << endl;
} else {
cerr << "FAILURE" << endl;
_globalT = _globalT*guess;
}
if (! (_counter%50) ) {
Eigen::Matrix3f R = _globalT.linear();
Eigen::Matrix3f E = R.transpose() * R;
E.diagonal().array() -= 1;
_globalT.linear() -= 0.5 * R * E;
}
_globalT.matrix().row(3) << 0,0,0,1;
cerr << "globalTransform : " << t2v(_globalT).transpose() << endl;
// char buf[1024];
// sprintf(buf, "frame-%05d.pwn",_counter);
// frame->save(buf, 1, true, _globalT);
cerr << "creating alias" << endl;
// create an alias for the previous node
MapNodeAlias* newRoot = new MapNodeAlias(_previousSensingFrameNode,_previousSensingFrameNode->manager());
_previousSensingFrameNode->manager()->addNode(newRoot);
cerr << "creating alias relation" << endl;
MapNodeAliasRelation* aliasRel = new MapNodeAliasRelation(newRoot,_previousSensingFrameNode->manager());
aliasRel->nodes()[0] = newRoot;
aliasRel->nodes()[1] = _previousSensingFrameNode;
_previousSensingFrameNode->manager()->addRelation(aliasRel);
cerr << "reparenting old elements" << endl;
// assign all the used relations to the alias node
if (imu){
imu->setOwner(newRoot);
imu->manager()->addRelation(imu);
}
if (odom){
odom->setOwner(newRoot);
odom->manager()->addRelation(imu);
}
cerr << "adding result" << endl;
Relation* newRel = new Relation(_aligner, _converter,
_previousSensingFrameNode, sensingFrame,
_topic, _aligner->inliers(), _aligner->error(), _manager);
newRel->setOwner(newRoot);
newRel->nodes()[0] = newRoot;
newRel->nodes()[1] = _previousSensingFrameNode;
Eigen::Isometry3d iso;
convertScalar(iso,_aligner->T());
newRel->setTransform(iso);
newRel->setInformationMatrix(Eigen::Matrix<double, 6,6>::Identity());
newRel->manager()->addRelation(newRel);
*os << _globalT.translation().transpose() << endl;
// create a new alias node for the prior element
// bind the alias with the prior node
// add to the alias the relations that have been used to
// determine the transformation
// add the alias to the map
// add the new transformation to the map
} else {
_aligner->setCurrentSensorOffset(sensorOffset);
_globalT = Eigen::Isometry3f::Identity();
if (imu){
Eigen::Isometry3f mean;
convertScalar(mean,imu->transform());
_globalT = mean;
}
}
cerr << "deleting previous frame" << endl;
if (_previousFrame)
delete _previousFrame;
cerr << "deleting blob frame" << endl;
delete blob;
cerr << "bookkeeping update" << endl;
_previousSensingFrameNode = sensingFrame;
_previousFrame = frame;
_counter++;
cerr << "END ITERATION" << endl;
}
