使用正态分布变换（NDT）算法对点云进行配准

在本教程中，我们将介绍如何使用正态分布变换（NDT）算法来确定两个大规模点云（都超过100,000个点）之间的刚性变换。NDT算法是一种配准算法，其使用应用于3D点的统计模型的标准优化技术来确定两个点云之间的最可能的配准。因为其在配准过程中不依赖对应点的特征计算和匹配，所以效率高。有关NDT算法内部工作原理的更多信息，请参阅Martin Magnusson博士的博士论文“The Three-Dimensional Normal Distributions Transform – an Efficient Representation for Registration, Surface Analysis, and Loop Detection”。

代码

首先，下载数据集room_scan1.pcd和room_scan2.pcd并将它们保存到磁盘。这些点云包含从不同角度对同一房间的360度扫描。

然后，在您喜欢的编辑器中创建一个文件，并将以下内容放入其中。我在本教程中使用了normal_distributions_transform.cpp。

#include <iostream>
#include <thread>

#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>

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::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
  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);
}

解释说明

这部分我就不翻译了，直接看代码和原文就好。

主要包括：

• 添加需要的头文件
• 加载目标点云和源点云
• 使用近似体素网格滤波器对源点云进行下采样，提高配准效率
• 初始化NDT，设置参数，参数要根据自己的数据进行调整
• 给NDT算法提供一个初始估计
• 保存点云
• 可视化

编译和运行

将以下内容添加到CMakeLists.txt文件中：

cmake_minimum_required(VERSION 2.8 FATAL_ERROR)

project(normal_distributions_transform)

find_package(PCL 1.5 REQUIRED)

include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})

add_executable(normal_distributions_transform normal_distributions_transform.cpp)
target_link_libraries (normal_distributions_transform ${PCL_LIBRARIES})

然后，编译并运行：

./normal_distributions_transform

您应该看到与下面类似的结果以及对齐点云的可视化。

Loaded 112586 data points from room_scan1.pcd
Loaded 112624 data points from room_scan2.pcd
Filtered cloud contains 12433 data points from room_scan2.pcd
Normal Distributions Transform has converged:1 score: 0.638694

注意：计算量有点大，电脑配置低的要耐心等会儿才会出结果。

参考

http://pointclouds.org/documentation/tutorials/normal_distributions_transform.php#normal-distributions-transform