#include <pcl/features/3dsc.h> ShapeContext3DEstimation (bool random=false) // 构造函数 virtual ~ShapeContext3DEstimation () // 析构函数 size_t getAzimuthBins () // 获得沿方向角划分的数目。 size_t getElevationBins () // 获得沿俯仰角划分的数目。 size_t getRadiusBins () // 获得沿径向...
size_t LimitingFilter(size_t (*get_data)(void)) { static size_t last_data = 0; //记录上一次获取的数据 size_t new_data = 0; size_t filter_out_data = 0; new_data = get_data(); //获得新数据变量 if((new_data - last_data) > A || (last_data - new_data > A)) { filte...
662#defineEIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)\663void*operatornew(std::size_tsize){\664returnEigen::internal::conditional_aligned_malloc<NeedsToAlign>(size);\...// 下面省略了其他 new/delete 等内存操作 终于最后落到了Eigen::internal::conditional_aligned_malloc之类的函数,从函数名可以...
std::cout << "size:" << cloud_out->points.size() << std::endl; //实现一个简单的点云刚体变换,以构造目标点云 for (size_t i = 0; i < cloud_in->points.size (); ++i) cloud_out->points[i].x = cloud_in->points[i].x + 0.7f; std::cout << "Transformed " << cloud_in...
for (size_t jj = 0; jj < _cloud2->points.size(); ++jj){pcl::visualization::PointCloudColorHandlerGenericField<pcl::PointXYZRGB> fildColor(_cloud_pl, "z");_viewer->addPointCloud(_cloud_pl, fildColor, "_cloud_pl");pviewer->setPointCloudRenderingProperties(pcl::visualization::PCL_...
setNegative (false); // 存储分割得到的平面上的点到点云文件 pcl::PointCloud<PointT>::Ptr cloud_plane (new pcl::PointCloud<PointT> ()); extract.filter (*cloud_plane); std::cerr << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points....
size_t 整型,保存一个整数,记录一个大小(size) points.size() 表示点云数据大小 (2)数据可视化 #include<iostream>#include<pcl/io/pcd_io.h>#include<pcl/point_types.h>#include<pcl/visualization/cloud_viewer.h>intmain(intargc,char**argv) { ...
cloud->points.resize (cloud->width * cloud->height);//生成数据,采用随机数填充点云的x,y坐标,都处于z为1的平面上for(size_t i =0; i < cloud->points.size (); ++i) { cloud->points[i].x =1024* rand () / (RAND_MAX +1.0f); ...
sensor_msgs::PointCloud2 globalMap_pcd;//对 PointCloud 的操作与对 points 的操作是一样的:size_tsize1 = pointcloud.size();boolflag1 = pointcloud.empty();// 等价操作size_tsize2 = pointcloud.points.size();boolflag2 = pointcloud.points.empty(); ...
std::cout << "size:" << cloud_out->points.size() << std::endl; //实现一个简单的点云刚体变换,以构造目标点云 for (size_t i = 0; i < cloud_in->points.size (); ++i) cloud_out->points[i].x = cloud_in->points[i].x + 0.7f; ...