最近在看PCL的教程,发现对点云中具体数据点的访问也有好几种方式,看着看着就会混淆了,所以,现将每种方式记录下来,做个对比,方便随时复习,温故知新。
一、
第一种是在看《How to create a range image from a point cloud》教程时看到的,代码如下,这种方式是一种vector的赋值方式,首先将point数据push_back到pcl::PointXYZ类型的模板中,然后再用无序点云的方式重新组织点云数据。
pcl::PointCloud<pcl::PointXYZ> pointCloud; // Generate the data for (float y=-0.5f; y<=0.5f; y+=0.01f) { for (float z=-0.5f; z<=0.5f; z+=0.01f) { pcl::PointXYZ point; point.x = 2.0f - y; point.y = y; point.z = z; pointCloud.points.push_back(point); } } pointCloud.width = (uint32_t) pointCloud.points.size(); pointCloud.height = 1;二、
出处《Filtering a PointCloud using a PassThrough filter》,指针型类模板,采用“->points[i]”方式赋值。
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (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 filtering: " << 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;未完待续。。。