官方文档：Point Cloud Library (PCL): Module common

中文说明：

下面所有的程序共用如下的CmakeLists.txt文件

// CMakeLists.txt 文件
// 后面将projectname安装自己的名字更改
cmake_minimum_required(VERSION 2.6)
project(projectname)find_package(PCL 1.10 REQUIRED)
include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})add_executable(projectname projectname.cpp)
target_link_libraries (projectname ${PCL_LIBRARIES} )install(TARGETS projectname RUNTIME DESTINATION bin)

（1）angle角度转换

在pcl/common/angel.h文件中，有三个函数实现

// 从弧度到角度
pcl::rad2deg(float alpha) 
// 从角度到弧度
pcl::deg2rad(float aipha)
// 正则化角度在（-PI，PI）之间
pcl::normAngle(float alpha)

#include <iostream>
#include <pcl/common/angles.h>
using namespace std;int main()
{float alpha = 30;cout << pcl::deg2rad(alpha) << "-" << 30.0*3.14159/180 << endl;double beta = pcl::deg2rad(alpha)*2;cout << pcl::rad2deg(beta) << endl;return 0;
}

（2）distance距离计算

在distance头文件中总共有五个函数，包括 lineToLineSegment 、sqrPointToLineDistance 、getMaxSegment、squaredEuclideanDistance 、euclideanDistance这五个函数。

// 获取两条三维直线之间的最短三维线段
pcl::lineToLineSegment (const Eigen::VectorXf &line_a, const Eigen::VectorXf &line_b, Eigen::Vector4f &pt1_seg, Eigen::Vector4f &pt2_seg)
// 在给定的一组点中获得最大分段，并返回最小和最大点。
pcl::getMaxSegment (const pcl::PointCloud< PointT > &cloud, PointT &pmin, PointT &pmax)
// 获取点到线的平方距离（由点和方向表示）
pcl::sqrPointToLineDistance (const Eigen::Vector4f &pt, const Eigen::Vector4f &line_pt, const Eigen::Vector4f &line_dir)
// 欧氏距离平方求解
pcl::squaredEuclideanDistance (const PointType1& p1, const PointType2& p2)
// 欧式距离求解
euclideanDistance (const PointType1& p1, const PointType2& p2)

#include <iostream>
#include <pcl/point_cloud.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/common/distances.h>using namespace std;int main()
{pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer);viewer->setWindowName("PCL Distance Demo");#if 0// 1. lineToLineSegmentEigen::VectorXf line_a(6),line_b(6);Eigen::Vector4f pt1_seg, pt2_seg;line_a << -5,0,0,10,0,0;line_b << 0,-5,5,0,10,0;pcl::ModelCoefficients line_coeff;line_coeff.values.resize (6);// line_aline_coeff.values[0] = line_a(0);line_coeff.values[1] = line_a(1);line_coeff.values[2] = line_a(2);line_coeff.values[3] = line_a(3);line_coeff.values[4] = line_a(4);line_coeff.values[5] = line_a(5);viewer->addLine(line_coeff,"line_a");// line_bline_coeff.values[0] = line_b(0);line_coeff.values[1] = line_b(1);line_coeff.values[2] = line_b(2);line_coeff.values[3] = line_b(3);line_coeff.values[4] = line_b(4);line_coeff.values[5] = line_b(5);viewer->addLine(line_coeff,"line_b");pcl::lineToLineSegment(line_a,line_b,pt1_seg, pt2_seg);viewer->addLine(pcl::PointXYZ(pt1_seg.x(),pt1_seg.y(),pt1_seg.z()),pcl::PointXYZ(pt2_seg.x(),pt2_seg.y(),pt2_seg.z()),1.0,0,0,"lineseg");// 2. sqrPointToLineDistancedouble disSqr =  pcl::sqrPointToLineDistance(pt1_seg,Eigen::Vector4f(line_b(0),line_b(1),line_b(2),0),Eigen::Vector4f(line_b(3),line_b(4),line_b(5),0));cout << "pcl::sqrPointToLineDistance: " << disSqr << endl;// 3. squaredEuclideanDistancefloat dis = pcl::squaredEuclideanDistance(pcl::PointXYZ(pt1_seg.x(),pt1_seg.y(),pt1_seg.z()),pcl::PointXYZ(pt2_seg.x(),pt2_seg.y(),pt2_seg.z()));cout << "pcl::squaredEuclideanDistance: " << dis << endl;// 4. euclideanDistancedis = pcl::euclideanDistance(pcl::PointXYZ(pt1_seg.x(),pt1_seg.y(),pt1_seg.z()),pcl::PointXYZ(pt2_seg.x(),pt2_seg.y(),pt2_seg.z()));cout << "pcl::squaredEuclideanDistance: " << dis << endl;#else// 5. getMaxSegmentpcl::PointCloud<pcl::PointXYZRGB> cloud;pcl::io::loadPCDFile("../pig1.pcd",cloud);pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> rgb(cloud.makeShared());viewer->addPointCloud(cloud.makeShared(),rgb);pcl::PointXYZRGB pmin,pmax;double maxseg = pcl::getMaxSegment(cloud,pmin,pmax);cout << "pcl::getMaxSegment: " << maxseg << endl;viewer->addArrow(pmin,pmax,1.0,0.0,1.0,0,1.0,1.0);
#endifwhile(!viewer->wasStopped())viewer->spinOnce(100);return 0;
}

（3）random随机数生成

//高斯噪声产生器
pcl::common::CloudGenerator<pcl::PointXYZ, pcl::common::NormalGenerator<float> > generator;  
//均匀分布噪声产生器
pcl::common::UniformGenerator<pcl::PointXYZ, pcl::common::NormalGenerator<float> > 
//生成随机种子  
uint32_t seed = static_cast<uint32_t> (time(NULL));                                         //根据参数添加x方向的噪声
pcl::common::NormalGenerator<float>::Parameters x_params(xmean, xstddev, seed++);
generator.setParametersForX(x_params);                                                  
//根据参数添加y方向的噪声   
pcl::common::NormalGenerator<float>::Parameters y_params(ymean, ystddev, seed++);
generator.setParametersForY(y_params);                                                 
//根据参数添加z方向的噪声    
pcl::common::NormalGenerator<float>::Parameters z_params(zmean, zstddev, seed++);
generator.setParametersForZ(z_params);                                                   //产生等数据量的随机噪声    
generator.fill((*cloud).width, (*cloud).height, *gauss_cloud);                                

#include <iostream>
#include <ctime>
#include <pcl/common/random.h>
#include <pcl/point_cloud.h>
#include <pcl/visualization/pcl_visualizer.h>using namespace std;int main()
{pcl::PointCloud<pcl::PointXYZ> ncloud,ucloud;uint32_t seed = static_cast<uint32_t> (time(NULL));pcl::common::NormalGenerator<float> normal(0,5,seed);for(int i = 0; i < 2000; ++i){ncloud.push_back(pcl::PointXYZ(normal.run(),normal.run(),normal.run()));}pcl::common::UniformGenerator<float> uniform(0,10,seed);for(int i = 0; i < 2000; ++i){ucloud.push_back(pcl::PointXYZ(uniform.run()+10,uniform.run(),uniform.run()));}pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer());viewer->setWindowName("PCL Random Test");pcl::visualization::PointCloudColorHandlerGenericField<pcl::PointXYZ> rgb(ncloud.makeShared(),"z");viewer->addPointCloud(ncloud.makeShared(),rgb,"normal");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE,4,"normal");viewer->addPointCloud(ucloud.makeShared(),rgb,"uniform");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE,4,"uniform");while(!viewer->wasStopped())viewer->spinOnce(100);return 0;
}

（4）sping扩展模块

// 按行扩展
pcl::common::expandRows (const PointCloud<PointT>& input, PointCloud<PointT>& output,const PointT& val, const std::size_t& amount)
// 按列扩展
pcl::common::expandColumns (const PointCloud<PointT>& input, PointCloud<PointT>& output, const PointT& val, const std::size_t& amount)
// 复制行
pcl::common::duplicateRows (const PointCloud<PointT>& input, PointCloud<PointT>& output,const std::size_t& amount)
// 复制列
pcl::common::duplicateColumns (const PointCloud<PointT>& input, PointCloud<PointT>& output,const std::size_t& amount)
// 删除行
pcl::common::deleteRows (const PointCloud<PointT>& input, PointCloud<PointT>& output,const std::size_t& amount)
// 删除列
pcl::common::deleteCols (const PointCloud<PointT>& input, PointCloud<PointT>& output,const std::size_t& amount)
// 镜像翻转行
pcl::common::mirrorRows (const PointCloud<PointT>& input, PointCloud<PointT>& output,const std::size_t& amount)
// 镜像翻转列
pcl::common::mirrorColumns (const PointCloud<PointT>& input, PointCloud<PointT>& output,const std::size_t& amount)

#include <iostream>
#include <pcl/common/spring.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>using namespace std;void printCloud(pcl::PointCloud<pcl::PointXYZ> &cloud)
{int nrow = cloud.height;int ncol = cloud.width;for(int i = 0; i < nrow; ++i){for(int j = 0; j < ncol; ++j){cout << "(" << cloud.at(i*ncol + j).x << "," <<  cloud.at(i*ncol + j).y << "," << cloud.at(i*ncol + j).z << ") ";}cout << endl;}
}int main()
{pcl::PointCloud<pcl::PointXYZ> cloud;for(int irow = 0; irow < 3; ++irow){for(int icol = 0; icol < 4; ++icol){cloud.push_back(pcl::PointXYZ(irow,icol,irow+icol));}}cloud.width = 3;cloud.height = 4;cout << cloud;printCloud(cloud);pcl::PointCloud<pcl::PointXYZ> ocloud;pcl::common::expandRows(cloud,ocloud,pcl::PointXYZ(99,99,99),2);cout << ocloud;printCloud(ocloud);cout << "----------------------------------" << endl;pcl::common::duplicateColumns(cloud,ocloud,1);cout << ocloud;printCloud(ocloud);cout << "----------------------------------" << endl;pcl::common::deleteRows(ocloud,ocloud,1);cout << ocloud;printCloud(ocloud);cout << "----------------------------------" << endl;pcl::common::mirrorRows(ocloud,cloud,1);cout << cloud;printCloud(cloud);return 0;
}

（5）time获取时间模块

#include <iostream>
#include <pcl/common/time.h>
using namespace std;int main()
{pcl::StopWatch watch;pcl::EventFrequency freq;{pcl::ScopeTime scope("test for");int res;for(int i = 0; i < 1000000000; ++i){res = i*i;if(i%100 == 0) freq.event();}}cout << watch.getTime() << "ms - "  << watch.getTimeSeconds() << "s" << endl;cout << (uint32_t)freq.getFrequency() << endl;DO_EVERY(20,[]{ cout << "test do every" << endl;}());return 0;
}

（6）vector_average计算加权平均值和协方差矩阵

#include <iostream>
#include <pcl/common/vector_average.h>
using namespace std;int main()
{pcl::VECtorpcl::VectorAverage<float,4> vec;pcl::VectorAverage<float,4>::VectorType sample1;sample1 << 1,2,3,4;vec.add(sample1);Eigen::Vector4f sample2;sample2 << 2,5,7,1;vec.add(sample2,2);Eigen::Matrix<float, 4, 1> sample3;sample3 << 0,7,8,4;vec.add(sample3);cout << vec.getMean() << endl << vec.getCovariance() << endl;Eigen::Vector4f v,v1,v2,v3;vec.doPCA(v,v1,v2,v3);cout << v << endl << v1 << endl << v2 << endl << v3 << endl;return 0;
}

（7）time_trigger计时触发器

#include <iostream>
#include <pcl/common/time_trigger.h>
using namespace std;static int cnt = 0;
struct callBack
{void operator() (){cout << "callBack " << cnt++ << endl;}
};int main()
{// 定义计时触发器类pcl::TimeTrigger triger/*(1,callBack())*/;triger.setInterval(0.5);triger.registerCallback(callBack());triger.start();// 将主线程暂停100sstd::this_thread::sleep_for(std::chrono::seconds(100));return 0;
}

（8）colors颜色生成函数

#include <iostream>
#include <pcl/common/colors.h>
#include <array>using namespace std;int main()
{srand(time(nullptr));pcl::RGB rgb = pcl::getRandomColor(0.5,1.5);cout << "(" << (int)rgb.r << "," << (int)rgb.b << "," << (int)rgb.g  << "," << (int)rgb.a << ")" << endl;pcl::ColorLUT<pcl::LUT_GLASBEY> glut; //等价于pcl::GlasbeyLUT glut;pcl::ColorLUT<pcl::LUT_VIRIDIS> vlut; //等价于pcl::ViridisLUT vlut;cout << glut.at(127) << endl << vlut.at(127) << endl << glut.size() << " " << vlut.size() << endl;cout << pcl::ColorLUT<pcl::LUT_GLASBEY>::at(100) << endl << pcl::ViridisLUT::at(100) << endl;return 0;
}

（9）centroid点云计算

// 计算点云质心
pcl::compute3DCentroid(*cloud_smoothed,centroid); 
// 计算点云协方差矩阵
pcl::computeCovarianceMatrix<pcl::PointXYZRGB,double>
// 计算点云标准化协方差矩阵
pcl::computeCovarianceMatrixNormalized(*cloud,centroid1,covariance_matrix);

#include <iostream>
#include <pcl/point_cloud.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/io/pcd_io.h>
#include <pcl/common/centroid.h>
#include <Eigen/Eigen>using namespace std;int main()
{pcl::PointCloud<pcl::PointXYZRGB> cloud;pcl::io::loadPCDFile("../pig.pcd",cloud);Eigen::Matrix<float, 4, 1> centroid;pcl::compute3DCentroid<pcl::PointXYZRGB,float>(cloud,centroid);cout << "float centroid: \n" << centroid << endl;Eigen::Matrix<double, 4, 1> centroid1;pcl::compute3DCentroid<pcl::PointXYZRGB,double>(cloud,centroid1);cout << "double centroid: \n" << centroid1 << endl;Eigen::Matrix<double, 3, 3> covariance_matrix;pcl::computeCovarianceMatrix<pcl::PointXYZRGB,double>(cloud,centroid1,covariance_matrix);cout << "double covariance_matrix: \n" << covariance_matrix << endl;pcl::computeCovarianceMatrixNormalized<pcl::PointXYZRGB,double>(cloud,centroid1,covariance_matrix);cout << "double covariance_matrix Normalized: \n" << covariance_matrix << endl;pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer());pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> rgb(cloud.makeShared());viewer->addPointCloud(cloud.makeShared(),rgb);while (!viewer->wasStopped()) {viewer->spinOnce(1000);}return 0;
}

（10）transforms点云变换模块

#include <iostream>
#include <pcl/point_cloud.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/io/pcd_io.h>
#include <pcl/common/transforms.h>
#include <pcl/common/centroid.h>using namespace std;using PCRGB = pcl::PointCloud<pcl::PointXYZRGB>;void transform1(PCRGB &in_cloud,PCRGB &out_cloud)
{
#if 0Eigen::Affine3f transform = Eigen::Affine3f::Identity();transform.translation() << 20,-10,10;transform.rotate(Eigen::AngleAxisf(M_PI/4,Eigen::Vector3f::UnitX()));
#elseEigen::Transform<float,3,Eigen::Affine> transform = Eigen::Transform<float,3,Eigen::Affine>::Identity();transform.translation() << 10,-5,5;transform.rotate(Eigen::AngleAxisf(M_PI/4,Eigen::Vector3f::UnitX()));transform.scale(2);
#endifcout << transform.matrix() << endl;pcl::transformPointCloud(in_cloud,out_cloud,transform);
}void transform2(PCRGB &in_cloud,PCRGB &out_cloud)
{
#if 0Eigen::Matrix4f transform = Eigen::Matrix4f::Identity();float theta = M_PI/2;
//    transform(0,0) *= 2;transform(1,1) = cos(theta);transform(2,2) = cos(theta);transform(2,1) = sin(theta);transform(1,2) = -sin(theta);transform(0,3) = 20;transform(1,3) = -10;transform(2,3) = 20;
#elseEigen::Matrix<float, 4, 4> transform = Eigen::Matrix<float, 4, 4>::Identity();float theta = M_PI/2;transform(0,0) *= 1;transform(1,1) = 1*cos(theta);transform(2,2) = 1*cos(theta);transform(2,1) = 1*sin(theta);transform(1,2) = -1*sin(theta);transform(0,3) = 10;transform(1,3) = -5;transform(2,3) = 10;
#endifpcl::transformPointCloud(in_cloud,out_cloud,transform);
}void transform3(PCRGB &in_cloud,PCRGB &out_cloud)
{
#if 0Eigen::Vector3f offset(20,-10,10);Eigen::Quaternionf rotation(Eigen::AngleAxisf(-M_PI/2,Eigen::Vector3f::UnitX()));
#elseEigen::Matrix<float,3,1> offset(10,-5,5);Eigen::Quaternion<float> rotation(Eigen::AngleAxisf(-M_PI/2,Eigen::Vector3f::UnitX()));
#endifpcl::transformPointCloud(in_cloud,out_cloud,offset,rotation);
}void trans(PCRGB &in_cloud,PCRGB &out_cloud,int type)
{switch(type){case 1:transform1(in_cloud,out_cloud);break;case 2:transform2(in_cloud,out_cloud);break;case 3:transform3(in_cloud,out_cloud);break;default:break;}
}int main()
{PCRGB in_cloud,out_cloud;pcl::io::loadPCDFile("../pig.pcd",in_cloud);Eigen::Matrix<float, 4, 1> centroid;pcl::compute3DCentroid(in_cloud,centroid);Eigen::Affine3f transform0 = Eigen::Affine3f::Identity();transform0.translation() << -centroid(0),-centroid(1),-centroid(2);pcl::transformPointCloud(in_cloud,in_cloud,transform0);trans(in_cloud,out_cloud,2);pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer());pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> rgb(in_cloud.makeShared());viewer->addPointCloud(in_cloud.makeShared(),rgb,"in_cloud");pcl::visualization::PointCloudColorHandlerGenericField<pcl::PointXYZRGB> rgbt(out_cloud.makeShared(),"z");viewer->addPointCloud(out_cloud.makeShared(),rgbt,"out_cloud");while(!viewer->wasStopped()){viewer->spinOnce(100);}return 0;
}

common模块中的基本函数pcl::rad2deg(fllat alpha) 
从弧度到角度pcl::deg2rad(float aipha)
从角度到弧度pcl::normAngle(float alpha)
正则化角度在（-PI，PI）之间pcl::compute3DCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 4, 1 > &centroid)
计算给定一群点的3D中心点，并且返回一个三维向量pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix)
计算给定的三维点云的协方差矩阵。pcl::computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &centroid
计算正则化的3*3的协方差矩阵以及给定点云数据的中心点pcl::demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const Eigen::Matrix< Scalar, 4, 1 > &centroid, pcl::PointCloud< PointT > &cloud_out)pcl::computeNDCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > &centroid)
利用一组点的指数对其进行一般的、通用的nD中心估计。pcl::getAngle3D (const Eigen::Vector4f &v1, const Eigen::Vector4f &v2, const bool in_degree=false)
计算两个向量之间的角度pcl::getMeanStd (const std::vector< float > &values, double &mean, double &stddev)
同时计算给定点云数据的均值和标准方差pcl::getPointsInBox (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, std::vector< int > &indices)
在给定边界的情况下，获取一组位于框中的点pcl::getMaxDistance (const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
给定点云数据中点与点之间的最大距离的值pcl::getMinMax3D (const pcl::PointCloud< PointT > &cloud, PointT &min_pt, PointT &max_pt)
获取给定点云中的在XYZ轴上的最大和最小值pcl::getCircumcircleRadius (const PointT &pa, const PointT &pb, const PointT &pc)
计算由三个点pa、pb和pc构成的三角形的外接圆半径。pcl::getMinMax (const PointT &histogram, int len, float &min_p, float &max_p)
获取点直方图上的最小值和最大值。pcl::calculatePolygonArea (const pcl::PointCloud< PointT > &polygon)
根据给定的多边形的点云计算多边形的面积pcl::copyPoint (const PointInT &point_in, PointOutT &point_out)
从Point_in把字段数据赋值到Point_outpcl::lineToLineSegment (const Eigen::VectorXf &line_a, const Eigen::VectorXf &line_b, Eigen::Vector4f &pt1_seg, Eigen::Vector4f &pt2_seg)
获取两条三维直线之间的最短三维线段pcl::sqrPointToLineDistance (const Eigen::Vector4f &pt, const Eigen::Vector4f &line_pt, const Eigen::Vector4f &line_dir)
获取点到线的平方距离（由点和方向表示）pcl::getMaxSegment (const pcl::PointCloud< PointT > &cloud, PointT &pmin, PointT &pmax)
在给定的一组点中获得最大分段，并返回最小和最大点。pcl::eigen22 (const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
确定最小特征值及其对应的特征向量pcl::computeCorrespondingEigenVector (const Matrix &mat, const typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
确定对称半正定输入矩阵给定特征值对应的特征向量pcl::eigen33 (const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
确定对称半正定输入矩阵最小特征值的特征向量和特征值pcl::invert2x2 (const Matrix &matrix, Matrix &inverse)
计算2x2矩阵的逆。pcl::invert3x3SymMatrix (const Matrix &matrix, Matrix &inverse)
计算3x3对称矩阵的逆。pcl::determinant3x3Matrix (const Matrix &matrix)
计算3x3矩阵的行列式pcl::getTransFromUnitVectorsZY (const Eigen::Vector3f &z_axis, const Eigen::Vector3f &y_direction, Eigen::Affine3f &transformation)
获得唯一 的3D旋转，将Z轴旋转成（0，0，1）Y轴旋转成（0,1,0）并且两个轴是正交的。pcl::getTransformationFromTwoUnitVectorsAndOrigin (const Eigen::Vector3f &y_direction, const Eigen::Vector3f &z_axis, const Eigen::Vector3f &origin, Eigen::Affine3f &transformation)
得到将origin转化为（0，0，0）的变换，并将Z轴旋转成（0，0，1）和Y方向（0，1，0）pcl::getEulerAngles (const Eigen::Transform< Scalar, 3, Eigen::Affine > &t, Scalar &roll, Scalar &pitch, Scalar &yaw)
从给定的变换矩阵中提取欧拉角pcl::getTranslationAndEulerAngles (const Eigen::Transform< Scalar, 3, Eigen::Affine > &t, Scalar &x, Scalar &y, Scalar &z, Scalar &roll, Scalar &pitch, Scalar &yaw)
给定的转换中，提取XYZ以及欧拉角pcl::getTransformation (float x, float y, float z, float roll, float pitch, float yaw)
从给定的平移和欧拉角创建转换矩阵pcl::saveBinary (const Eigen::MatrixBase< Derived > &matrix, std::ostream &file)
保存或者写矩阵到一个输出流中pcl::loadBinary (Eigen::MatrixBase< Derived > const &matrix, std::istream &file)
从输入流中读取矩阵pcl::lineWithLineIntersection (const Eigen::VectorXf &line_a, const Eigen::VectorXf &line_b, Eigen::Vector4f &point, double sqr_eps=1e-4)
获取空间中两条三维直线作为三维点的交点。pcl::getFieldIndex (const pcl::PCLPointCloud2 &cloud, const std::string &field_name)
获取指定字段的索引（即维度/通道）pcl::getFieldsList (const pcl::PointCloud< PointT > &cloud)
获取给定点云中所有可用字段的列表pcl::getFieldSize (const int datatype)
获取特定字段数据类型的大小（字节）。pcl::concatenatePointCloud (const pcl::PCLPointCloud2 &cloud1, const pcl::PCLPointCloud2 &cloud2, pcl::PCLPointCloud2 &cloud_out)连接 pcl::PCLPointCloud2类型的点云字段

参考文章

（1）官方文档：Point Cloud Library (PCL): Module common

（2）博客：PCL基础介绍_像话吗的博客-CSDN博客