安装
安装环境：windows11 + vs2022 + camke
pcl安装包下载地址： Releases · PointCloudLibrary/pcl (github.com)
下载PCL-1.14.1-AllInOne-msvc2022-win64.exe并安装
配置环境变量
重启电脑，PCL安装完成。
创建示例项目
使用vs2022创建cmake项目
项目文件如下：
│ CMakeLists.txt
└─PointCloudAnalysis
CMakeLists.txt
PointCloudAnalysis.cpp
最外层的CMakeLists.txt文件内容如下：
# CMakeList.txt: 顶层 CMake 项目文件，在此处执行全局配置
# 并包含子项目。
#
cmake_minimum_required (VERSION 3.8)
# 如果支持，请为 MSVC 编译器启用热重载。
if (POLICY CMP0141)
cmake_policy(SET CMP0141 NEW)
set(CMAKE_MSVC_DEBUG_INFORMATION_FORMAT "$<IF:$<AND:$<C_COMPILER_ID:MSVC>,$<CXX_COMPILER_ID:MSVC>>,$<$<CONFIG:Debug,RelWithDebInfo>:EditAndContinue>,$<$<CONFIG:Debug,RelWithDebInfo>:ProgramDatabase>>")
endif()
project ("PointCloudAnalysis")
# 包含子项目。
add_subdirectory ("PointCloudAnalysis")
PointCloudAnalysis/CMakeLists.txt内容如下：
# CMakeList.txt: PointCloudAnalysis 的 CMake 项目，在此处包括源代码并定义
# 项目特定的逻辑。
#
# 将源代码添加到此项目的可执行文件。
add_executable (PointCloudAnalysis "PointCloudAnalysis.cpp" )
if (CMAKE_VERSION VERSION_GREATER 3.12)
set_property(TARGET PointCloudAnalysis PROPERTY CXX_STANDARD 20)
endif()
# TODO: 如有需要，请添加测试并安装目标。
find_package(PCL)
MESSAGE( STATUS "PCL_INCLUDE_DIRS = ${PCL_INCLUDE_DIRS}.")
include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})
target_link_libraries (PointCloudAnalysis ${PCL_LIBRARIES})
PointCloudAnalysis.cpp内容如下：
// PointCloudAnalysis.cpp: 定义应用程序的入口点。
//
#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/io/ply_io.h>
#include <pcl/point_cloud.h>
#include <pcl/console/parse.h>
#include <pcl/common/transforms.h> // 点云坐标变换
#include <pcl/visualization/pcl_visualizer.h>
// This function displays the help
void showHelp(char* program_name)
{
std::cout << std::endl;
std::cout << "Usage: " << program_name << " cloud_filename.[pcd|ply]" << std::endl;
std::cout << "-h: Show this help." << std::endl;
}
// This is the main function
int main(int argc, char** argv)
{
// Show help 展示帮助信息
if (pcl::console::find_switch(argc, argv, "-h") || pcl::console::find_switch(argc, argv, "--help"))
{
showHelp(argv[0]);
return 0;
}
// Fetch point cloud filename in arguments | Works with PCD and PLY files
std::vector<int> filenames;
bool file_is_pcd = false;
filenames = pcl::console::parse_file_extension_argument(argc, argv, ".ply");
if (filenames.size() != 1)
{
filenames = pcl::console::parse_file_extension_argument(argc, argv, ".pcd");
if (filenames.size() != 1)
{
showHelp(argv[0]);
return -1;
}
else
{
file_is_pcd = true;
}
}
// Load file | Works with PCD and PLY files 在参数中寻找pcd或者ply文件
pcl::PointCloud<pcl::PointXYZ>::Ptr source_cloud(new pcl::PointCloud<pcl::PointXYZ>());
if (file_is_pcd)
{
if (pcl::io::loadPCDFile(argv[filenames[0]], *source_cloud) < 0)
{
std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl
<< std::endl;
showHelp(argv[0]);
return -1;
}
}
else
{
if (pcl::io::loadPLYFile(argv[filenames[0]], *source_cloud) < 0)
{
std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl
<< std::endl;
showHelp(argv[0]);
return -1;
}
}
/* Reminder: how transformation matrices work :
|-------> This column is the translation
| 1 0 0 x | \
| 0 1 0 y | }-> The identity 3x3 matrix (no rotation) on the left
| 0 0 1 z | /
| 0 0 0 1 | -> We do not use this line (and it has to stay 0,0,0,1)
METHOD #1: Using a Matrix4f ====================================第二种方法
This is the "manual" method, perfect to understand but error prone !
*/
// 创建4x4单位阵
Eigen::Matrix4f transform_1 = Eigen::Matrix4f::Identity();
// Define a rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)
float theta = M_PI / 4; // The angle of rotation in radians
transform_1(0, 0) = std::cos(theta);
transform_1(0, 1) = -sin(theta);
transform_1(1, 0) = sin(theta);
transform_1(1, 1) = std::cos(theta);
// (row, column)
// Define a translation of 2.5 meters on the x axis.
transform_1(0, 3) = 2.5;
// Print the transformation
printf("Method #1: using a Matrix4f\n");
std::cout << transform_1 << std::endl;
/* METHOD #2: Using a Affine3f ====================================第二种方法
This method is easier and less error prone
*/
Eigen::Affine3f transform_2 = Eigen::Affine3f::Identity();
// Define a translation of 2.5 meters on the x axis.
transform_2.translation() << 2.5, 0.0, 0.0;
// The same rotation matrix as before; theta radians around Z axis
transform_2.rotate(Eigen::AngleAxisf(theta, Eigen::Vector3f::UnitZ()));
// Print the transformation
printf("\nMethod #2: using an Affine3f\n");
std::cout << transform_2.matrix() << std::endl;
// Executing the transformation
pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud(new pcl::PointCloud<pcl::PointXYZ>());
// You can either apply transform_1 or transform_2; they are the same
pcl::transformPointCloud(*source_cloud, *transformed_cloud, transform_2);
// Visualization 可视化
printf("\nPoint cloud colors : white = original point cloud\n"
" red = transformed point cloud\n");
pcl::visualization::PCLVisualizer viewer("Matrix transformation example");
// Define R,G,B colors for the point cloud
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> source_cloud_color_handler(source_cloud, 255, 255, 255);
// We add the point cloud to the viewer and pass the color handler
viewer.addPointCloud(source_cloud, source_cloud_color_handler, "original_cloud");
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> transformed_cloud_color_handler(transformed_cloud, 230, 20, 20); // Red
viewer.addPointCloud(transformed_cloud, transformed_cloud_color_handler, "transformed_cloud");
viewer.addCoordinateSystem(1.0, "cloud", 0);
viewer.setBackgroundColor(0.05, 0.05, 0.05, 0); // Setting background to a dark grey
viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "original_cloud");
viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "transformed_cloud");
//viewer.setPosition(800, 400); // Setting visualiser window position
while (!viewer.wasStopped())
{ // Display the visualiser until 'q' key is pressed
viewer.spinOnce();
}
return 0;
}
点击编译，编译成功后进入\out\build\x64-Debug\PointCloudAnalysis目录，运行exe文件
我们需要一个（pcd|ply）格式的点云文件（可以从data/terrain at master · PointCloudLibrary/data (github.com)下载）
重新运行exe文件传入下载的点云文件 PointCloudAnalysis.exe XXXXXX.pcd 可以看到下面的效果
