作者经历了从PCL1.7-PCL1.9,VS2013-VS2017的反复配置，其过程是坎坷与辛酸，踩过各种坑，现在配置起来真的是炉火纯青了。现在又来台新电脑了，没错，再来配置一次吧。此次配置VS2017+PCL1.9.1
准备工作
- VS2017 自行下载
-
PCL1.9.1
点击链接跳转Github下载，根据自己的环境选择win64或win32,作者下载的是：PCL-1.9.1-AllInOne-msvc2017-win64.exe
pcl-1.9.1-pdb-msvc2017-win64.zip
安装PCL
双击点开PCL-1.9.1-AllInOne-msvc2017-win64.exe，选择Add PCL to the system PATH for all users,
更改安装路径，
E:\Program Files\PCL 1.9.1,
选择第三方库，这里作者全选，开始安装，
中途跳出OpenNI安装，选择路径
./3rdParty/OpenNI2/,安装，
安装完毕。
解压pcl-1.9.1-pdb-msvc2017-win64.zip，将PDB文件拷贝到PCL安装路径下的bin文件夹中。
环境配置
-
系统环境配置
计算机->属性->高级系统变量->环境变量，双击Path，
添加如下路径(路径根据自己的安装目录添加)
添加完毕后，电脑注销后生效。
- VS开发环境配置
- 打开VS2017，新建空项目
2.菜单栏点击视图->其他窗口->属性管理器，选择releaseordebug,win32orx64，这里以release|x64为例，右键新建添加新项目属性表，取名config,添加，
3.双击新建的属性表
VC++ 目录->包含目录->添加头文件：(根据自己pcl的路径添加)
E:\Program Files\PCL 1.9.1\include\pcl-1.9
E:\Program Files\PCL 1.9.1\3rdParty\Boost\include\boost-1_68
E:\Program Files\PCL 1.9.1\3rdParty\Eigen\eigen3
E:\Program Files\PCL 1.9.1\3rdParty\FLANN\include
E:\Program Files\PCL 1.9.1\3rdParty\OpenNI2\Include
E:\Program Files\PCL 1.9.1\3rdParty\Qhull\include
E:\Program Files\PCL 1.9.1\3rdParty\VTK\include\vtk-8.1
VC++ 目录->库目录->添加库文件：(根据自己pcl的路径添加)
E:\Program Files\PCL 1.9.1\lib
E:\Program Files\PCL 1.9.1\3rdParty\Boost\lib
E:\Program Files\PCL 1.9.1\3rdParty\FLANN\lib
E:\Program Files\PCL 1.9.1\3rdParty\OpenNI2\Lib
E:\Program Files\PCL 1.9.1\3rdParty\Qhull\lib
E:\Program Files\PCL 1.9.1\3rdParty\VTK\lib
链接器->输入->附加依赖项添加lib文件：(这里是release版本),如果你上面选择的是debug win32 or debug x64,则添加debug版本的lib文件，请看下面的Tips,教你如何获取lib目录。
pcl_common_release.lib
pcl_features_release.lib
pcl_filters_release.lib
pcl_io_ply_release.lib
pcl_io_release.lib
pcl_kdtree_release.lib
pcl_keypoints_release.lib
pcl_ml_release.lib
pcl_octree_release.lib
pcl_outofcore_release.lib
pcl_people_release.lib
pcl_recognition_release.lib
pcl_registration_release.lib
pcl_sample_consensus_release.lib
pcl_search_release.lib
pcl_segmentation_release.lib
pcl_stereo_release.lib
pcl_surface_release.lib
pcl_tracking_release.lib
pcl_visualization_release.lib
libboost_atomic-vc141-mt-x64-1_68.lib
libboost_bzip2-vc141-mt-x64-1_68.lib
libboost_chrono-vc141-mt-x64-1_68.lib
libboost_container-vc141-mt-x64-1_68.lib
libboost_context-vc141-mt-x64-1_68.lib
libboost_contract-vc141-mt-x64-1_68.lib
libboost_coroutine-vc141-mt-x64-1_68.lib
libboost_date_time-vc141-mt-x64-1_68.lib
libboost_exception-vc141-mt-x64-1_68.lib
libboost_fiber-vc141-mt-x64-1_68.lib
libboost_filesystem-vc141-mt-x64-1_68.lib
libboost_graph-vc141-mt-x64-1_68.lib
libboost_graph_parallel-vc141-mt-x64-1_68.lib
libboost_iostreams-vc141-mt-x64-1_68.lib
libboost_locale-vc141-mt-x64-1_68.lib
libboost_log-vc141-mt-x64-1_68.lib
libboost_log_setup-vc141-mt-x64-1_68.lib
libboost_math_c99-vc141-mt-x64-1_68.lib
libboost_math_c99f-vc141-mt-x64-1_68.lib
libboost_math_c99l-vc141-mt-x64-1_68.lib
libboost_math_tr1-vc141-mt-x64-1_68.lib
libboost_math_tr1f-vc141-mt-x64-1_68.lib
libboost_math_tr1l-vc141-mt-x64-1_68.lib
libboost_mpi-vc141-mt-x64-1_68.lib
libboost_numpy27-vc141-mt-x64-1_68.lib
libboost_numpy37-vc141-mt-x64-1_68.lib
libboost_prg_exec_monitor-vc141-mt-x64-1_68.lib
libboost_program_options-vc141-mt-x64-1_68.lib
libboost_python27-vc141-mt-x64-1_68.lib
libboost_python37-vc141-mt-x64-1_68.lib
libboost_random-vc141-mt-x64-1_68.lib
libboost_regex-vc141-mt-x64-1_68.lib
libboost_serialization-vc141-mt-x64-1_68.lib
libboost_signals-vc141-mt-x64-1_68.lib
libboost_stacktrace_noop-vc141-mt-x64-1_68.lib
libboost_stacktrace_windbg-vc141-mt-x64-1_68.lib
libboost_stacktrace_windbg_cached-vc141-mt-x64-1_68.lib
libboost_system-vc141-mt-x64-1_68.lib
libboost_test_exec_monitor-vc141-mt-x64-1_68.lib
libboost_thread-vc141-mt-x64-1_68.lib
libboost_timer-vc141-mt-x64-1_68.lib
libboost_type_erasure-vc141-mt-x64-1_68.lib
libboost_unit_test_framework-vc141-mt-x64-1_68.lib
libboost_wave-vc141-mt-x64-1_68.lib
libboost_wserialization-vc141-mt-x64-1_68.lib
libboost_zlib-vc141-mt-x64-1_68.lib
OpenNI2.lib
flann.lib
flann_cpp.lib
flann_cpp_s.lib
flann_s.lib
qhull.lib
qhullcpp.lib
qhullstatic.lib
qhullstatic_r.lib
qhull_p.lib
qhull_r.lib
vtkalglib-8.1.lib
vtkChartsCore-8.1.lib
vtkCommonColor-8.1.lib
vtkCommonComputationalGeometry-8.1.lib
vtkCommonCore-8.1.lib
vtkCommonDataModel-8.1.lib
vtkCommonExecutionModel-8.1.lib
vtkCommonMath-8.1.lib
vtkCommonMisc-8.1.lib
vtkCommonSystem-8.1.lib
vtkCommonTransforms-8.1.lib
vtkDICOMParser-8.1.lib
vtkDomainsChemistry-8.1.lib
vtkexoIIc-8.1.lib
vtkexpat-8.1.lib
vtkFiltersAMR-8.1.lib
vtkFiltersCore-8.1.lib
vtkFiltersExtraction-8.1.lib
vtkFiltersFlowPaths-8.1.lib
vtkFiltersGeneral-8.1.lib
vtkFiltersGeneric-8.1.lib
vtkFiltersGeometry-8.1.lib
vtkFiltersHybrid-8.1.lib
vtkFiltersHyperTree-8.1.lib
vtkFiltersImaging-8.1.lib
vtkFiltersModeling-8.1.lib
vtkFiltersParallel-8.1.lib
vtkFiltersParallelImaging-8.1.lib
vtkFiltersPoints-8.1.lib
vtkFiltersProgrammable-8.1.lib
vtkFiltersSelection-8.1.lib
vtkFiltersSMP-8.1.lib
vtkFiltersSources-8.1.lib
vtkFiltersStatistics-8.1.lib
vtkFiltersTexture-8.1.lib
vtkFiltersTopology-8.1.lib
vtkFiltersVerdict-8.1.lib
vtkfreetype-8.1.lib
vtkGeovisCore-8.1.lib
vtkgl2ps-8.1.lib
vtkhdf5-8.1.lib
vtkhdf5_hl-8.1.lib
vtkImagingColor-8.1.lib
vtkImagingCore-8.1.lib
vtkImagingFourier-8.1.lib
vtkImagingGeneral-8.1.lib
vtkImagingHybrid-8.1.lib
vtkImagingMath-8.1.lib
vtkImagingMorphological-8.1.lib
vtkImagingSources-8.1.lib
vtkImagingStatistics-8.1.lib
vtkImagingStencil-8.1.lib
vtkInfovisCore-8.1.lib
vtkInfovisLayout-8.1.lib
vtkInteractionImage-8.1.lib
vtkInteractionStyle-8.1.lib
vtkInteractionWidgets-8.1.lib
vtkIOAMR-8.1.lib
vtkIOCore-8.1.lib
vtkIOEnSight-8.1.lib
vtkIOExodus-8.1.lib
vtkIOExport-8.1.lib
vtkIOExportOpenGL-8.1.lib
vtkIOGeometry-8.1.lib
vtkIOImage-8.1.lib
vtkIOImport-8.1.lib
vtkIOInfovis-8.1.lib
vtkIOLegacy-8.1.lib
vtkIOLSDyna-8.1.lib
vtkIOMINC-8.1.lib
vtkIOMovie-8.1.lib
vtkIONetCDF-8.1.lib
vtkIOParallel-8.1.lib
vtkIOParallelXML-8.1.lib
vtkIOPLY-8.1.lib
vtkIOSQL-8.1.lib
vtkIOTecplotTable-8.1.lib
vtkIOVideo-8.1.lib
vtkIOXML-8.1.lib
vtkIOXMLParser-8.1.lib
vtkjpeg-8.1.lib
vtkjsoncpp-8.1.lib
vtklibharu-8.1.lib
vtklibxml2-8.1.lib
vtklz4-8.1.lib
vtkmetaio-8.1.lib
vtkNetCDF-8.1.lib
vtknetcdfcpp-8.1.lib
vtkoggtheora-8.1.lib
vtkParallelCore-8.1.lib
vtkpng-8.1.lib
vtkproj4-8.1.lib
vtkRenderingAnnotation-8.1.lib
vtkRenderingContext2D-8.1.lib
vtkRenderingContextOpenGL-8.1.lib
vtkRenderingCore-8.1.lib
vtkRenderingFreeType-8.1.lib
vtkRenderingGL2PS-8.1.lib
vtkRenderingImage-8.1.lib
vtkRenderingLabel-8.1.lib
vtkRenderingLIC-8.1.lib
vtkRenderingLOD-8.1.lib
vtkRenderingOpenGL-8.1.lib
vtkRenderingVolume-8.1.lib
vtkRenderingVolumeOpenGL-8.1.lib
vtksqlite-8.1.lib
vtksys-8.1.lib
vtktiff-8.1.lib
vtkverdict-8.1.lib
vtkViewsContext2D-8.1.lib
vtkViewsCore-8.1.lib
vtkViewsInfovis-8.1.lib
vtkzlib-8.1.lib
Tips：如何获得lib目录？
举个例子：我们要获得.\PCL 1.9.1\lib目录下的lib目录，在此文件夹下新建一个.txt文本，在文本中写入下列代码，保存。
dir /b *debug.lib>1.txt
将文本后缀名.txt改为.bat，运行脚本，
在目录下会生成
1.txt，打开即为lib目录（这里提取的是debug目录，如需release，只需修改代码，dir /b *release.lib>1.txt）。
至此，配置完成！
程序测试
#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/ModelCoefficients.h>
#include <pcl/filters/project_inliers.h>
int main(int argc, char** argv)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected(new pcl::PointCloud<pcl::PointXYZ>);
// Fill in the cloud data
cloud->width = 5;
cloud->height = 1;
cloud->points.resize(cloud->width * cloud->height);
for (size_t i = 0; i < cloud->points.size(); ++i)
{
cloud->points[i].x = 1024 * rand() / (RAND_MAX + 1.0f);
cloud->points[i].y = 1024 * rand() / (RAND_MAX + 1.0f);
cloud->points[i].z = 1024 * rand() / (RAND_MAX + 1.0f);
}
std::cerr << "Cloud before projection: " << std::endl;
for (size_t i = 0; i < cloud->points.size(); ++i)
std::cerr << " " << cloud->points[i].x << " "
<< cloud->points[i].y << " "
<< cloud->points[i].z << std::endl;
// Create a set of planar coefficients with X=Y=0,Z=1
pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients());
coefficients->values.resize(4);
coefficients->values[0] = coefficients->values[1] = 0;
coefficients->values[2] = 1.0;
coefficients->values[3] = 0;
// Create the filtering object
pcl::ProjectInliers<pcl::PointXYZ> proj;
proj.setModelType(pcl::SACMODEL_PLANE);
proj.setInputCloud(cloud);
proj.setModelCoefficients(coefficients);
proj.filter(*cloud_projected);
std::cerr << "Cloud after projection: " << std::endl;
for (size_t i = 0; i < cloud_projected->points.size(); ++i)
std::cerr << " " << cloud_projected->points[i].x << " "
<< cloud_projected->points[i].y << " "
<< cloud_projected->points[i].z << std::endl;
system("pause");
return (0);
}
如果生成成功，并能运行得到结果，表明配置成功！
相关问题
如果出现如下报错，
解决方法：
双击自己建的属性表，
C/C++ -> 预处理器 -> 预处理器定义添加
_CRT_SECURE_NO_WARNINGS
_SCL_SECURE_NO_WARNINGS
_SILENCE_FPOS_SEEKPOS_DEPRECATION_WARNING
结语
至此，相信你也和我一样，已经配置成功了。
如有任何问题或是书写纰漏，请给我留言，我会帮你们耐心解决。
感谢观看，希望对你们有所帮助！
