3d激光雷达开发（旋转和位移）

对于点云数据来说，旋转和位移的计算是十分必要的。比如数据匹配、识别、定位，如果需要查看获得的旋转矩阵对不对，那么就可以将原来的数据和旋转矩阵做一个乘积，这样就可以立刻看到对应的效果了。

1、准备transform.cpp文件

#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>
#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 helpif (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 filesstd::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 filespcl::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 Matrix4fThis is the "manual" method, perfect to understand but error prone !*/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 radianstransform_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 transformationprintf ("Method #1: using a Matrix4f\n");std::cout << transform_1 << std::endl;/*  METHOD #2: Using a Affine3fThis 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 axistransform_2.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitZ()));// Print the transformationprintf ("\nMethod #2: using an Affine3f\n");std::cout << transform_2.matrix() << std::endl;// Executing the transformationpcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ> ());// You can either apply transform_1 or transform_2; they are the samepcl::transformPointCloud (*source_cloud, *transformed_cloud, transform_2);// Visualizationprintf(  "\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 cloudpcl::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 handlerviewer.addPointCloud (source_cloud, source_cloud_color_handler, "original_cloud");pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> transformed_cloud_color_handler (transformed_cloud, 230, 20, 20); // Redviewer.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 greyviewer.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 positionwhile (!viewer.wasStopped ()) { // Display the visualiser until 'q' key is pressedviewer.spinOnce ();}return 0;
}

2、代码说明

代码里面主要说明了两种构建旋转矩阵的方法。不管是哪一种，本质上都是要把yaw、pitch、roll、x、y、z通过计算映射到矩阵里面。

3、准备CMakeLists.txt

cmake_minimum_required(VERSION 2.8 FATAL_ERROR)project(transform)find_package(PCL 1.2 REQUIRED)include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})add_executable (transform transform.cpp)
target_link_libraries (transform ${PCL_LIBRARIES})

4、开始生成sln工程，准备编译，

5、执行transform.exe

执行过程中，注意输入参数，即transform.exe bunny.pcd。

另外，代码中应该是对点云数据x轴偏移2.5米，z轴旋转theta角度，工作台的打印如下，

实际效果如下，

3d激光雷达开发（旋转和位移）

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