PCL近邻搜索相关的类

首先PCL定义了搜索的基类pcl::search::Search<PointInT>

template<typename PointT>
    class Search

其子类包括：KD树，八叉树，FLANN快速搜索，暴力搜索（brute force），有序点云搜索。

The pcl_search library provides methods for searching for nearest neighbors using different data structures, including:

• kd-trees (via libpcl_kdtree);
• octrees (via libpcl_octree);
• brute force;
• specialized search for organized datasets.

search类都定义了两种最常见的近邻搜索模式：k近邻搜索，球状固定距离半径近邻搜索。

virtual int nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const = ;

virtual int  radiusSearch (const PointT& point, double radius, std::vector<int>& k_indices, std::vector<float>& k_sqr_distances, unsigned int max_nn = ) const = ;

还有一种比较有用的方式是：圆柱固定距离半径搜索，即在XOY平面的投影距离，目前没有看到PCL中的专门的实现方式，可以通过一种折中的方法解决octree。

还有就是通过累积地图实现的投影到XOY平面内的简单搜索。

Weinmann, M., et al. (2015). "Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers." ISPRS Journal of Photogrammetry and Remote Sensing 105: 286-304.

在feature模块中大量使用了近邻搜索的东西。近邻搜索是很多点云计算的基础功能。

示例

如下调用了点云KD树近邻搜索实现了8个基于特征值的点云特征计算：

     QString filePly = dlg.txtPath->text();
     std::wstring pszRoomFile1 = filePly.toStdWString();
     char buffer[];
     size_t ret = wcstombs(buffer, pszRoomFile1.c_str(), sizeof(buffer));
     const char * pszShapeFile = buffer;
     char * file_name = (char*)pszShapeFile;
     pcl::PointCloud<pcl::PointXYZ>::Ptr input_(new pcl::PointCloud<pcl::PointXYZ>);
     /*------------读取PLY文件-------------*/
     if (pcl::io::loadPLYFile<pcl::PointXYZ>(file_name, *input_) == -) //* load the file
     {
         PCL_ERROR("Couldn't read file test_pcd.pcd \n");
         QMessageBox::information(NULL, "Title", "Content", QMessageBox::Yes | QMessageBox::No, QMessageBox::Yes);
     }
     double search_radius_ = dlg.sb_scale2->value();
     int k_=;
     double search_parameter_ = 0.0;
     /*------------构造索引-------------*/
     boost::shared_ptr <std::vector<int> > indices_;
     if (!indices_)
     {
         indices_.reset(new std::vector<int>);
         try
         {
             indices_->resize(input_->points.size());
         }
         catch (const std::bad_alloc&)
         {
             PCL_ERROR("[initCompute] Failed to allocate %lu indices.\n", input_->points.size());
         }
         for (size_t i = ; i < indices_->size(); ++i) { (*indices_)[i] = static_cast<int>(i); }
     }
     std::vector<int> nn_indices(k_);
     std::vector<float> nn_dists(k_);
     /*---------------构造KD树-------------*/
     //pcl::search::Search<pcl::PointXYZ>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZ> >(new pcl::search::KdTree<pcl::PointXYZ>);
     pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
     tree->setInputCloud(input_);
     SearchMethodSurface search_method_surface_;
     if (search_radius_ != 0.0)
     {
         search_parameter_ = search_radius_;
         int (pcl::search::Search<pcl::PointXYZ>::*radiusSearchSurface)(const PointCloudIn &cloud, int index, double radius,
             std::vector<int> &k_indices, std::vector<float> &k_distances,
             unsigned int max_nn) const = &pcl::search::Search<pcl::PointXYZ>::radiusSearch;
         search_method_surface_ = boost::bind(radiusSearchSurface, boost::ref(tree), _1, _2, _3, _4, _5, );
     }
     else
     {
         search_parameter_ = k_;
         int (pcl::search::Search<pcl::PointXYZ>::*nearestKSearchSurface)(const PointCloudIn &cloud, int index, int k, std::vector<int> &k_indices,
             std::vector<float> &k_distances) const = &pcl::search::Search<pcl::PointXYZ>::nearestKSearch;
         search_method_surface_ = boost::bind(nearestKSearchSurface, boost::ref(tree), _1, _2, _3, _4, _5);
     }
     pcl::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>);
     PointCloudOut& output = *cloud_normals;
     AxPointCloudOut output_pts;
     if (input_->size() < )
     {
         return;
     }
     else/*--------------计算特征值和特征向量-------------*/
     {
         output_pts.resize(input_->size());
         //output_pts.reserve(input_->size(), (new AxPoint()));
 #ifdef _OPENMP
 #pragma omp parallel for shared (output_pts) private (nn_indices, nn_dists) num_threads(threads_)
 #endif
         // Iterating over the entire index vector
         for (int idx = ; idx < static_cast<int> (indices_->size()); ++idx)
         {
             if (search_method_surface_(*input_, (*indices_)[idx], search_parameter_, nn_indices, nn_dists) == )
             {
                 output.points[idx].normal[] = output.points[idx].normal[] = output.points[idx].normal[] = output.points[idx].curvature = std::numeric_limits<float>::quiet_NaN();

                 output.is_dense = false;
                 continue;
             }

             EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
             // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
             Eigen::Vector4f xyz_centroid;

             if (pcl::computeMeanAndCovarianceMatrix(*input_, nn_indices, covariance_matrix, xyz_centroid) == )
             {
                 continue;
             }

             EIGEN_ALIGN16 Eigen::Matrix3f eigen_vector3;
             EIGEN_ALIGN16 Eigen::Vector3f eigen_values;
             //计算特征值和特征向量
             pcl::eigen33(covariance_matrix, eigen_vector3, eigen_values);
             double eig_val1 = eigen_values[];
             double eig_val2 = eigen_values[];
             double eig_val3 = eigen_values[];

             output_pts[idx].x = input_->at((*indices_)[idx]).x;
             output_pts[idx].y = input_->at((*indices_)[idx]).y;
             output_pts[idx].z = input_->at((*indices_)[idx]).z;
             output_pts[idx].eig_val1 = eig_val1;
             output_pts[idx].eig_val2 = eig_val2;
             output_pts[idx].eig_val3 = eig_val3;
         }
         QString savefilePly = filePly.replace(".ply",".txt");
         std::wstring psaveFile1 = savefilePly.toStdWString();
         char buffer[];
         size_t ret = wcstombs(buffer, psaveFile1.c_str(), sizeof(buffer));
         const char * psavetxtFile = buffer;
         char * file_name_2 = (char*)psavetxtFile;
         FILE*  saveFeaturePointCloud = fopen(file_name_2, "w");
         for (int i = ; i < output_pts.size(); i++)
         {
             float x = output_pts[i].x;
             float y = output_pts[i].y;
             float z = output_pts[i].z;

             //注意：eig_val1最小
             float eig_val1 = output_pts[i].eig_val1;
             float eig_val2 = output_pts[i].eig_val2;
             float eig_val3 = output_pts[i].eig_val3;
             float eig_sum = eig_val1 + eig_val2 + eig_val3;

             float e1 = , e2 = , e3 = ;
             float Linearity = ;
             float Planarity = ;
             float Scattering = ;
             float Omnivariance = ;
             float Anisotropy = ;
             float EigenEntropy = ;
             float changeOfcurvature = ;
             if (eig_sum != )
             {
                 e1 = eig_val3 / eig_sum;
                 e2 = eig_val2 / eig_sum;
                 e3 = eig_val1 / eig_sum;
                 Linearity = (e1 - e2) / e1;
                 Planarity = (e2 - e3) / e1;
                 Scattering = e3 / e1;
                 Omnivariance = pow(e1*e2*e3,  / );
                 Anisotropy = (e1 - e3) / e1;
                 EigenEntropy = -(e1*log(e1) + e2*log(e2) + e3*log(e3));
                 //计算曲率变化
                 changeOfcurvature = fabsf(e1 / (e1 + e2 + e3));
             }
             else
                 changeOfcurvature = ;
             //x,y,z,e1,e2,e3,
             //Linearity,Planarity,Scattering,Omnivariance,Anisotropy,
             //Eigenentropy,Sum of eigenvalues,Change of curvature
             fprintf(saveFeaturePointCloud, "%f %f %f %f %f %f %f %f %f %f %f %f %f %f\n", x, y, z, eig_val1, eig_val2, eig_val3,
                 Linearity, Planarity, Scattering, Omnivariance, Anisotropy, EigenEntropy, eig_sum, changeOfcurvature);
         }
         fclose(saveFeaturePointCloud);
     }