std::vector<Quadric> HandSearch::findQuadrics(const PointCloud::Ptr cloud,
const Eigen::VectorXi& pts_cam_source, const pcl::KdTreeFLANN<pcl::PointXYZ>& kdtree,
const std::vector<int>& indices)
{
double t1 = omp_get_wtime();
std::vector<int> nn_indices;
std::vector<float> nn_dists;
std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> > T_cams;
T_cams.push_back(cam_tf_left_);
T_cams.push_back(cam_tf_right_);
std::vector<Quadric> quadric_list(indices.size());
#ifdef _OPENMP // parallelization using OpenMP
#pragma omp parallel for private(nn_indices, nn_dists) num_threads(num_threads_)
#endif
for (int i = 0; i < indices.size(); i++)
{
const pcl::PointXYZ& sample = cloud->points[indices[i]];
// std::cout << "i: " << i << ", index: " << indices[i] << ", sample: " << sample << std::endl;
if (kdtree.radiusSearch(sample, nn_radius_taubin_, nn_indices, nn_dists) > 0)
{
Eigen::VectorXi nn_cam_source(nn_indices.size());
// std::cout << " Found " << nn_indices.size() << " neighbors.\n";
for (int j = 0; j < nn_cam_source.size(); j++)
{
nn_cam_source(j) = pts_cam_source(nn_indices[j]);
}
Eigen::Vector3d sample_eigen = sample.getVector3fMap().cast<double>();
Quadric q(T_cams, cloud, sample_eigen, uses_determinstic_normal_estimation_);
q.fitQuadric(nn_indices);
// std::cout << " Fitted quadric\n";
q.findTaubinNormalAxis(nn_indices, nn_cam_source);
// std::cout << " Found local axes\n";
quadric_list[i] = q;
cloud_normals_.col(indices[i]) = q.getNormal();
}
}
double t2 = omp_get_wtime();
//std::cout << "Fitted " << quadric_list.size() << " quadrics in " << t2 - t1 << " sec.\n";
// quadric_list[0].print(); // debugging
// plot_.plotLocalAxes(quadric_list, cloud);
return quadric_list;
}
int main(int argc, char** argv)
{
double taubin_radius = 0.03; // radius of curvature-estimation neighborhood
double hand_radius = 0.08; // radius of hand configuration neighborhood
// std::string file = "/home/andreas/data/mlaffordance/round21l_reg.pcd";
std::string file = "/home/andreas/data/mlaffordance/training/rect31l_reg.pcd";
PointCloud::Ptr cloud(new PointCloud);
if (pcl::io::loadPCDFile<pcl::PointXYZ>(file, *cloud) == -1) //* load the file
{
PCL_ERROR("Couldn't read input PCD file\n");
return (-1);
}
pcl::search::OrganizedNeighbor<pcl::PointXYZ>::Ptr organized_neighbor(
new pcl::search::OrganizedNeighbor<pcl::PointXYZ>());
std::vector<int> nn_indices;
std::vector<float> nn_dists;
pcl::KdTreeFLANN<pcl::PointXYZ>::Ptr tree(new pcl::KdTreeFLANN<pcl::PointXYZ>());
// int sample_index = 0;
int sample_index = 731;
if (cloud->isOrganized())
{
organized_neighbor->setInputCloud(cloud);
organized_neighbor->radiusSearch(cloud->points[sample_index], taubin_radius, nn_indices, nn_dists);
}
else
{
tree->setInputCloud(cloud);
tree->radiusSearch(cloud->points[sample_index], taubin_radius, nn_indices, nn_dists);
}
std::cout << "Found point neighborhood for sample " << sample_index << "\n";
Eigen::Matrix4d T_base, T_sqrt;
T_base << 0, 0.445417, 0.895323, 0.22, 1, 0, 0, -0.02, 0, 0.895323, -0.445417, 0.24, 0, 0, 0, 1;
T_sqrt << 0.9366, -0.0162, 0.3500, -0.2863, 0.0151, 0.9999, 0.0058, 0.0058, -0.3501, -0.0002, 0.9367, 0.0554, 0, 0, 0, 1;
std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> > T_cams;
T_cams.push_back(T_base * T_sqrt.inverse());
std::cout << T_cams[0] << std::endl;
// fit quadric
Eigen::Vector3d sample = cloud->points[sample_index].getVector3fMap().cast<double>();
Quadric quadric(T_cams, cloud, sample, true);
quadric.fitQuadric(nn_indices);
Eigen::MatrixXi cam_source = Eigen::MatrixXi::Zero(1, cloud->points.size());
quadric.findTaubinNormalAxis(nn_indices, cam_source);
quadric.print();
// fit hand
tree->radiusSearch(cloud->points[sample_index], hand_radius, nn_indices, nn_dists);
Eigen::VectorXi pts_cam_source = Eigen::VectorXi::Ones(1, cloud->size());
Eigen::Matrix3Xd nn_normals(3, nn_indices.size());
Eigen::VectorXi nn_cam_source(nn_indices.size());
Eigen::Matrix3Xd centered_neighborhood(3, nn_indices.size());
nn_normals.setZero();
for (int j = 0; j < nn_indices.size(); j++)
{
nn_cam_source(j) = pts_cam_source(nn_indices[j]);
centered_neighborhood.col(j) = cloud->points[nn_indices[j]].getVector3fMap().cast<double>() - sample;
}
double finger_width_ = 0.01;
double hand_outer_diameter_ = 0.09;
double hand_depth_ = 0.06;
ParallelHand finger_hand(finger_width_, hand_outer_diameter_, hand_depth_);
double hand_height_ = 0.02;
double init_bite_ = 0.01;
RotatingHand rotating_hand(T_cams[0].block(0, 3, 3, 1) - sample,
T_cams[0].block(0, 3, 3, 1) - sample, finger_hand, true, pts_cam_source(sample_index));
rotating_hand.transformPoints(centered_neighborhood, quadric.getNormal(), quadric.getCurvatureAxis(), nn_normals, nn_cam_source,
hand_height_);
std::vector<GraspHypothesis> h = rotating_hand.evaluateHand(init_bite_, sample, 1);
for (int i = 0; i < h.size(); i++)
{
std::cout << "-- orientation " << i << " --\n";
h[i].print();
}
// std::cout << "\n";
// rotating_hand.evaluateHand(init_bite_, sample, 1);
// rotating_hand.print();
return 0;
}
std::vector<GraspHypothesis> HandSearch::findHands(const PointCloud::Ptr cloud,
const Eigen::VectorXi& pts_cam_source, const std::vector<Quadric>& quadric_list,
const Eigen::VectorXi& hands_cam_source, const pcl::KdTreeFLANN<pcl::PointXYZ>& kdtree)
{
double t1 = omp_get_wtime();
std::vector<int> nn_indices;
std::vector<float> nn_dists;
Eigen::Matrix3Xd nn_normals(3, nn_indices.size());
Eigen::VectorXi nn_cam_source(nn_indices.size());
Eigen::Matrix3Xd centered_neighborhood(3, nn_indices.size());
std::vector<RotatingHand> hand_list(quadric_list.size());
// std::vector<RotatingHand> hand_list;
double time_eval_hand = 0.0;
double time_iter = 0.0;
double time_nn = 0.0;
double time_tf = 0.0;
std::vector< std::vector<GraspHypothesis> > grasp_lists(quadric_list.size(), std::vector<GraspHypothesis>(0));
#ifdef _OPENMP // parallelization using OpenMP
#pragma omp parallel for private(nn_indices, nn_dists, nn_normals, nn_cam_source, centered_neighborhood) num_threads(num_threads_)
#endif
for (std::size_t i = 0; i < quadric_list.size(); i++)
{
double timei = omp_get_wtime();
pcl::PointXYZ sample;
sample.x = quadric_list[i].getSample()(0);
sample.y = quadric_list[i].getSample()(1);
sample.z = quadric_list[i].getSample()(2);
// std::cout << "i: " << i << ", sample: " << sample << std::endl;
if (kdtree.radiusSearch(sample, nn_radius_hands_, nn_indices, nn_dists) > 0)
{
time_nn += omp_get_wtime() - timei;
nn_normals.setZero(3, nn_indices.size());
nn_cam_source.setZero(nn_indices.size());
centered_neighborhood.setZero(3, nn_indices.size());
for (int j = 0; j < nn_indices.size(); j++)
{
nn_cam_source(j) = pts_cam_source(nn_indices[j]);
centered_neighborhood.col(j) = (cloud->points[nn_indices[j]].getVector3fMap()
- sample.getVector3fMap()).cast<double>();
nn_normals.col(j) = cloud_normals_.col(nn_indices[j]);
}
FingerHand finger_hand(finger_width_, hand_outer_diameter_, hand_depth_);
Eigen::Vector3d sample_eig = sample.getVector3fMap().cast<double>();
RotatingHand rotating_hand(cam_tf_left_.block<3, 1>(0, 3) - sample_eig,
cam_tf_right_.block<3, 1>(0, 3) - sample_eig, finger_hand, tolerant_antipodal_, hands_cam_source(i));
const Quadric& q = quadric_list[i];
double time_tf1 = omp_get_wtime();
rotating_hand.transformPoints(centered_neighborhood, q.getNormal(), q.getCurvatureAxis(), nn_normals,
nn_cam_source, hand_height_);
time_tf += omp_get_wtime() - time_tf1;
double time_eval1 = omp_get_wtime();
std::vector<GraspHypothesis> grasps = rotating_hand.evaluateHand(init_bite_, sample_eig, true);
time_eval_hand += omp_get_wtime() - time_eval1;
if (grasps.size() > 0)
{
// grasp_list.insert(grasp_list.end(), grasps.begin(), grasps.end());
grasp_lists[i] = grasps;
}
}
time_iter += omp_get_wtime() - timei;
}
time_eval_hand /= quadric_list.size();
time_nn /= quadric_list.size();
time_iter /= quadric_list.size();
time_tf /= quadric_list.size();
//std::cout << " avg time for transforming point neighborhood: " << time_tf << " sec.\n";
//std::cout << " avg time for NN search: " << time_nn << " sec.\n";
//std::cout << " avg time for rotating_hand.evaluate(): " << time_eval_hand << " sec.\n";
//std::cout << " avg time per iteration: " << time_iter << " sec.\n";
std::vector<GraspHypothesis> grasp_list;
for (std::size_t i = 0; i < grasp_lists.size(); i++)
{
// std::cout << i << " " << grasp_lists[i].size() << "\n";
if (grasp_lists[i].size() > 0)
grasp_list.insert(grasp_list.end(), grasp_lists[i].begin(), grasp_lists[i].end());
}
double t2 = omp_get_wtime();
//std::cout << " Found " << grasp_list.size() << " robot hand poses in " << t2 - t1 << " sec.\n";
return grasp_list;
}