PCL 学习笔记

9、源程序和3D点云：

cloud_viewer_PointXYZ.cpp如下： #include #include #include #include

int user_data; void

viewerOneOff (pcl::visualization::PCLVisualizer& viewer) {

viewer.setBackgroundColor (1.0, 0.5, 1.0); pcl::PointXYZ o; o.x = 1.0; o.y = 0; o.z = 0;

viewer.addSphere (o, 0.25, \ std::cout << \ } void

viewerPsycho (pcl::visualization::PCLVisualizer& viewer) {

static unsigned count = 0; std::stringstream ss;

ss << \ viewer.removeShape (\

viewer.addText (ss.str(), 200, 300, \可¨|以°?注á?é释o¨a掉ì?这a行D，ê?此??时o?à不?会¨￠再¨′显?示o?图a?形?中D显?示o?Once per viewer loop:num一°?直?à增?加¨?

//FIXME: possible race condition here: user_data++; } int main () {

pcl::visualization::CloudViewer viewer(\

//blocks until the cloud is actually rendered viewer.showCloud(cloud);

//use the following functions to get access to the underlying more advanced/powerful //PCLVisualizer

//This will only get called once

viewer.runOnVisualizationThreadOnce (viewerOneOff);

//This will get called once per visualization iteration viewer.runOnVisualizationThread (viewerPsycho); while (!viewer.wasStopped ()) {

//you can also do cool processing here

//FIXME: Note that this is running in a separate thread from viewerPsycho //and you should guard against race conditions yourself... user_data++; } return 0; }

pcl::io::loadPCDFile (\

pcl::PointCloud::Ptr cloud (new pcl::PointCloud); //modified

二、激光扫描仪；

立体摄像机和飞行摄像机获得的点云数据是有序点云数据，类似于图像或矩阵的结构，数据分为行和列；它相比于无序点云的优点在于预先了解相邻点的关系，邻域操作更加高效。