使用正态分布变换进行配准

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>

#include <pcl/registration/ndt.h>
#include <pcl/filters/approximate_voxel_grid.h>

#include <pcl/visualization/pcl_visualizer.h>
#include <boost/thread/thread.hpp>

int
main(int argc, char** argv)
{
// Loading first scan of room.
pcl::PointCloud<pcl::PointXYZ>::Ptr target_cloud(new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile<pcl::PointXYZ>("room_scan1.pcd", *target_cloud) == -1)
{
PCL_ERROR("Couldn't read file room_scan1.pcd \n");
return (-1);
}
std::cout << "Loaded " << target_cloud->size() << " data points from room_scan1.pcd" << std::endl;

// Loading second scan of room from new perspective.
pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud(new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile<pcl::PointXYZ>("room_scan2.pcd", *input_cloud) == -1)
{
PCL_ERROR("Couldn't read file room_scan2.pcd \n");
return (-1);
}
std::cout << "Loaded " << input_cloud->size() << " data points from room_scan2.pcd" << std::endl;

// Filtering input scan to roughly 10% of original size to increase speed of registration.
pcl::PointCloud<pcl::PointXYZ>::Ptr filtered_cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::ApproximateVoxelGrid<pcl::PointXYZ> approximate_voxel_filter;
approximate_voxel_filter.setLeafSize(0.2, 0.2, 0.2);
approximate_voxel_filter.setInputCloud(input_cloud);
approximate_voxel_filter.filter(*filtered_cloud);
std::cout << "Filtered cloud contains " << filtered_cloud->size()
<< " data points from room_scan2.pcd" << std::endl;

// Initializing Normal Distributions Transform (NDT).
pcl::NormalDistributionsTransform<pcl::PointXYZ, pcl::PointXYZ> ndt;

// Setting scale dependent NDT parameters
// Setting minimum transformation difference for termination condition.
ndt.setTransformationEpsilon(0.01);
// Setting maximum step size for More-Thuente line search.
ndt.setStepSize(0.1);
//Setting Resolution of NDT grid structure (VoxelGridCovariance).
ndt.setResolution(1.0);

// Setting max number of registration iterations.
ndt.setMaximumIterations(35);

// Setting point cloud to be aligned.
ndt.setInputSource(filtered_cloud);
// Setting point cloud to be aligned to.
ndt.setInputTarget(target_cloud);

// Set initial alignment estimate found using robot odometry.
Eigen::AngleAxisf init_rotation(0.6931, Eigen::Vector3f::UnitZ());
Eigen::Translation3f init_translation(1.79387, 0.720047, 0);
Eigen::Matrix4f init_guess = (init_translation * init_rotation).matrix();

// Calculating required rigid transform to align the input cloud to the target cloud.
pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud(new pcl::PointCloud<pcl::PointXYZ>);
ndt.align(*output_cloud, init_guess);

std::cout << "Normal Distributions Transform has converged:" << ndt.hasConverged()
<< " score: " << ndt.getFitnessScore() << std::endl;

// Transforming unfiltered, input cloud using found transform.
pcl::transformPointCloud(*input_cloud, *output_cloud, ndt.getFinalTransformation());

// Saving transformed input cloud.
pcl::io::savePCDFileASCII("room_scan2_transformed.pcd", *output_cloud);

// Initializing point cloud visualizer
boost::shared_ptr<pcl::visualization::PCLVisualizer>
viewer_final(new pcl::visualization::PCLVisualizer("3D Viewer"));
viewer_final->setBackgroundColor(0, 0, 0);

// Coloring and visualizing target cloud (red).
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>
target_color(target_cloud, 255, 0, 0);
viewer_final->addPointCloud<pcl::PointXYZ>(target_cloud, target_color, "target cloud");
viewer_final->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE,
1, "target cloud");

// Coloring and visualizing transformed input cloud (green).
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>
output_color(output_cloud, 0, 255, 0);
viewer_final->addPointCloud<pcl::PointXYZ>(output_cloud, output_color, "output cloud");
viewer_final->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE,
1, "output cloud");

// Starting visualizer
viewer_final->addCoordinateSystem(1.0, "global");
viewer_final->initCameraParameters();

// Wait until visualizer window is closed.
while (!viewer_final->wasStopped())
{
viewer_final->spinOnce(100);
boost::this_thread::sleep(boost::posix_time::microseconds(100000));
}

return (0);
}