计算点云法向量
1.先mark一个文件操作：遍历（或者迭代遍历）指定目录，boost::filesystem可真好用
1 for (const auto& it : boost::filesystem::directory_iterator("/your/path")) { 2 if (it.path().extension() == ".pcd") { 3 std::cout << it.path() << ", " << it.path().filename() << ", " << it.path().stem() << ", " << it.path().extension() << std::endl; 4 } 5 } 6 7 for (const auto& it : boost::filesystem::recursive_directory_iterator("/your/path")) { 8 if (it.path().extension() == ".pcd") { 9 std::cout << it.path() << ", " << it.path().filename() << ", " << it.path().stem() << ", " << it.path().extension() << std::endl; 10 } 11 }
// it.path() it.path().filename() it.path().stem() it.path().extension()
// "/path/pairs_12_7.pcd", "pairs_12_7.pcd", "pairs_12_7", ".pcd"
2.用pcl::NormalEstimation简直就是坑爹，计算出的点云法向量有40~50%都是有问题的
1 pcl::search::KdTree<PointS>::Ptr kdtree_submap(new pcl::search::KdTree<PointS>); 2 kdtree_submap->setInputCloud(cloud_submap);// Make sure the tree searches the surface 3 pcl::NormalEstimation<PointS, PointS>::Ptr ne(new pcl::NormalEstimation<PointS, PointS>); 4 ne->setInputCloud(cloud_ds_angle_); 5 ne->setSearchSurface(cloud_submap); 6 ne->setSearchMethod(kdtree_submap); 7 ne->setRadiusSearch(search_r_ne); 8 ne->compute(*cloud_ds_angle_);
用pca和kdtree自己计算，效果赞赞赞，而且效率与上面的一样
1 void my_normal_estimation(const KdTreePtr &kdtree, PCloudTPtr &cloud, double search_r) { 2 for (auto &pt : cloud->points) { 3 std::vector<int> k_indices; 4 std::vector<float> k_sqr_distances; 5 if (kdtree->radiusSearch(pt, search_r, k_indices, k_sqr_distances) < 3) { 6 continue; 7 } 8 PCloudTPtr cloud_search(new PCloudT); 9 pcl::copyPointCloud(*(kdtree->getInputCloud()), k_indices, *cloud_search); 10 pcl::PCA<PointT> pca; 11 pca.setInputCloud(cloud_search); 12 Eigen::Matrix3f eigen_vector = pca.getEigenVectors(); 13 Eigen::Vector3f vect_2 = eigen_vector.col(2);// please fit to your own coordinate 14 pt.normal_x = vect_2[0]; 15 pt.normal_y = vect_2[1]; 16 pt.normal_z = vect_2[2]; 17 } 18 }
