系统ubuntu14.04

#include <iostream>#include <pcl/ModelCoefficients.h>#include <pcl/io/pcd_io.h>#include <pcl/filters/project_inliers.h>#include <pcl/filters/extract_indices.h>#include <pcl/sample_consensus/method_types.h>#include <pcl/sample_consensus/model_types.h>#include <pcl/segmentation/sac_segmentation.h>#include <pcl/visualization/cloud_viewer.h>#include <pcl/point_types.h>#include <pcl/filters/voxel_grid.h>#include <pcl/filters/passthrough.h>#include <pcl/features/normal_3d.h>#include <pcl/filters/radius_outlier_removal.h>#include <pcl/kdtree/kdtree_flann.h>#include <pcl/segmentation/extract_clusters.h>#include <Eigen/Core>#include <pcl/common/transforms.h>#include <pcl/common/common.h>using namespace std;typedef pcl::PointXYZ PointType;int main(int argc, char **argv){pcl::PointCloud<PointType>::Ptr cloud(new pcl::PointCloud<PointType>());pcl::io::loadPCDFile("table_scene_lms400.pcd", *cloud);Eigen::Vector4f pcaCentroid;pcl::compute3DCentroid(*cloud, pcaCentroid);Eigen::Matrix3f covariance;pcl::computeCovarianceMatrixNormalized(*cloud, pcaCentroid, covariance);Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eigen_solver(covariance, Eigen::ComputeEigenvectors);Eigen::Matrix3f eigenVectorsPCA = eigen_solver.eigenvectors();Eigen::Vector3f eigenValuesPCA = eigen_solver.eigenvalues();eigenVectorsPCA.col(2) = eigenVectorsPCA.col(0).cross(eigenVectorsPCA.col(1)); //校正主方向间垂直eigenVectorsPCA.col(0) = eigenVectorsPCA.col(1).cross(eigenVectorsPCA.col(2));eigenVectorsPCA.col(1) = eigenVectorsPCA.col(2).cross(eigenVectorsPCA.col(0));std::cout << "特征值va(3x1):\n" << eigenValuesPCA << std::endl;std::cout << "特征向量ve(3x3):\n" << eigenVectorsPCA << std::endl;std::cout << "质心点(4x1):\n" << pcaCentroid << std::endl;/*// 另一种计算点云协方差矩阵特征值和特征向量的方式:通过pcl中的pca接口，如下，这种情况得到的特征向量相似特征向量pcl::PointCloud<pcl::PointXYZ>::Ptr cloudPCAprojection (new pcl::PointCloud<pcl::PointXYZ>);pcl::PCA<pcl::PointXYZ> pca;pca.setInputCloud(cloudSegmented);pca.project(*cloudSegmented, *cloudPCAprojection);std::cerr << std::endl << "EigenVectors: " << pca.getEigenVectors() << std::endl;//计算特征向量std::cerr << std::endl << "EigenValues: " << pca.getEigenValues() << std::endl;//计算特征值*/Eigen::Matrix4f tm = Eigen::Matrix4f::Identity();Eigen::Matrix4f tm_inv = Eigen::Matrix4f::Identity();tm.block<3, 3>(0, 0) = eigenVectorsPCA.transpose(); //R.tm.block<3, 1>(0, 3) = -1.0f * (eigenVectorsPCA.transpose()) *(pcaCentroid.head<3>());// -R*ttm_inv = tm.inverse();std::cout << "变换矩阵tm(4x4):\n" << tm << std::endl;std::cout << "逆变矩阵tm'(4x4):\n" << tm_inv << std::endl;pcl::PointCloud<PointType>::Ptr transformedCloud(new pcl::PointCloud<PointType>);pcl::transformPointCloud(*cloud, *transformedCloud, tm);PointType min_p1, max_p1;Eigen::Vector3f c1, c;pcl::getMinMax3D(*transformedCloud, min_p1, max_p1);c1 = 0.5f*(min_p1.getVector3fMap() + max_p1.getVector3fMap());std::cout << "型心c1(3x1):\n" << c1 << std::endl;Eigen::Affine3f tm_inv_aff(tm_inv);pcl::transformPoint(c1, c, tm_inv_aff);Eigen::Vector3f whd, whd1;whd1 = max_p1.getVector3fMap() - min_p1.getVector3fMap();whd = whd1;float sc1 = (whd1(0) + whd1(1) + whd1(2)) / 3; //点云平均尺度，用于设置主方向箭头大小std::cout << "width1=" << whd1(0) << endl;std::cout << "heght1=" << whd1(1) << endl;std::cout << "depth1=" << whd1(2) << endl;std::cout << "scale1=" << sc1 << endl;const Eigen::Quaternionf bboxQ1(Eigen::Quaternionf::Identity());const Eigen::Vector3f bboxT1(c1);const Eigen::Quaternionf bboxQ(tm_inv.block<3, 3>(0, 0));const Eigen::Vector3f bboxT(c);//变换到原点的点云主方向PointType op;op.x = 0.0;op.y = 0.0;op.z = 0.0;Eigen::Vector3f px, py, pz;Eigen::Affine3f tm_aff(tm);pcl::transformVector(eigenVectorsPCA.col(0), px, tm_aff);pcl::transformVector(eigenVectorsPCA.col(1), py, tm_aff);pcl::transformVector(eigenVectorsPCA.col(2), pz, tm_aff);PointType pcaX;pcaX.x = sc1 * px(0);pcaX.y = sc1 * px(1);pcaX.z = sc1 * px(2);PointType pcaY;pcaY.x = sc1 * py(0);pcaY.y = sc1 * py(1);pcaY.z = sc1 * py(2);PointType pcaZ;pcaZ.x = sc1 * pz(0);pcaZ.y = sc1 * pz(1);pcaZ.z = sc1 * pz(2);//初始点云的主方向PointType cp;cp.x = pcaCentroid(0);cp.y = pcaCentroid(1);cp.z = pcaCentroid(2);PointType pcX;pcX.x = sc1 * eigenVectorsPCA(0, 0) + cp.x;pcX.y = sc1 * eigenVectorsPCA(1, 0) + cp.y;pcX.z = sc1 * eigenVectorsPCA(2, 0) + cp.z;PointType pcY;pcY.x = sc1 * eigenVectorsPCA(0, 1) + cp.x;pcY.y = sc1 * eigenVectorsPCA(1, 1) + cp.y;pcY.z = sc1 * eigenVectorsPCA(2, 1) + cp.z;PointType pcZ;pcZ.x = sc1 * eigenVectorsPCA(0, 2) + cp.x;pcZ.y = sc1 * eigenVectorsPCA(1, 2) + cp.y;pcZ.z = sc1 * eigenVectorsPCA(2, 2) + cp.z;//visualizationpcl::visualization::PCLVisualizer viewer;pcl::visualization::PointCloudColorHandlerCustom<PointType> tc_handler(transformedCloud, 0, 255, 0); //转换到原点的点云相关viewer.addPointCloud(transformedCloud, tc_handler, "transformCloud");viewer.addCube(bboxT1, bboxQ1, whd1(0), whd1(1), whd1(2), "bbox1");viewer.setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_REPRESENTATION, pcl::visualization::PCL_VISUALIZER_REPRESENTATION_WIREFRAME, "bbox1");viewer.setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0.0, 1.0, 0.0, "bbox1");viewer.addArrow(pcaX, op, 1.0, 0.0, 0.0, false, "arrow_X");viewer.addArrow(pcaY, op, 0.0, 1.0, 0.0, false, "arrow_Y");viewer.addArrow(pcaZ, op, 0.0, 0.0, 1.0, false, "arrow_Z");pcl::visualization::PointCloudColorHandlerCustom<PointType> color_handler(cloud, 255, 0, 0); //输入的初始点云相关viewer.addPointCloud(cloud, color_handler, "cloud");viewer.addCube(bboxT, bboxQ, whd(0), whd(1), whd(2), "bbox");viewer.setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_REPRESENTATION, pcl::visualization::PCL_VISUALIZER_REPRESENTATION_WIREFRAME, "bbox");viewer.setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 0.0, 0.0, "bbox");viewer.addArrow(pcX, cp, 1.0, 0.0, 0.0, false, "arrow_x");viewer.addArrow(pcY, cp, 0.0, 1.0, 0.0, false, "arrow_y");viewer.addArrow(pcZ, cp, 0.0, 0.0, 1.0, false, "arrow_z");viewer.addCoordinateSystem(0.5f*sc1);viewer.setBackgroundColor(0.0, 0.0, 0.0);while (!viewer.wasStopped()){viewer.spinOnce();}return 0;}