How to use normal distribution transformation for registration

（11.1.3 PCL Study ） How to use normal distribution transformation for registration

Code ： The first 11 Chapter one 3 Folder
Report errors 1：
Assertion failed: point_representation_->isValid (point) && “Invalid (NaN, Inf) point coordinates given to radiusSearch!”, file C:\BuildAgent\work\1cb946cef51fc766\tags\pcl-1.6.0\kdtree\include\pcl/kdtree/impl/kdtree_flann.hpp, line 119
resolvent ：
Find the problem ：https://ask.csdn.net/questions/252527
Did not remove the NaN spot , You need to add two sentences after loading the point cloud file to remove the NaN Point code
Reference resources ：https://blog.csdn.net/qq_36501182/article/details/79170438
removeNaNFromPointCloud The use of the class
pcl::removeNaNFromPointCloud(laserCloudIn, laserCloudIn, indices); The function has three arguments , Input point clouds , Output the point cloud and the corresponding reserved index .
load pcd Add... After the file ：

    std::vector<int>indices;
    pcl::removeNaNFromPointCloud(*target_cloud, *target_cloud, indices);

Be careful target_cloud Corresponding to the named file name
Error reporting solution .
The complete code is as follows ：

#include <iostream>
#include <thread>

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

#include <pcl/registration/ndt.h>//NDT The registration class corresponds to the header file 
#include <pcl/filters/approximate_voxel_grid.h>// The filter class corresponds to the header file 

#include <pcl/visualization/pcl_visualizer.h>

using namespace std::chrono_literals;

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::vector<int>indices;
    pcl::removeNaNFromPointCloud(*target_cloud, *target_cloud, indices);

    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::vector<int>indices;*/
    pcl::removeNaNFromPointCloud(*input_cloud, *input_cloud, indices);
    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);// Set the maximum number of matching iterations , Before reaching this limit, the optimization program will be in epsilon Terminate at the transformation threshold 

    // Setting point cloud to be aligned.
    ndt.setInputSource(filtered_cloud);// Set source point cloud 
    // Setting point cloud to be aligned to.
    ndt.setInputTarget(target_cloud);// Set the target point 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
    pcl::visualization::PCLVisualizer::Ptr
        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);
        std::this_thread::sleep_for(100ms);
    }

    return (0);
}

Running results ：

Obviously, the registration of two point cloud files is unsuccessful