如何使用迭代最近点（How to use iterative closest point）

#如何使用迭代最近点
本文档演示如何在代码中使用迭代最接近点算法，通过最小化两个点云之间的距离并严格转换它们，可以确定一个PointCloud是否只是另一个PointCloud的刚性转换。

#代码

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/icp.h>int
main (int argc, char** argv)
{pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in (new pcl::PointCloud<pcl::PointXYZ>);pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZ>);// Fill in the CloudIn datacloud_in->width    = 5;cloud_in->height  = 1;cloud_in->is_dense = false;cloud_in->points.resize (cloud_in->width * cloud_in->height);for (size_t i = 0; i < cloud_in->points.size (); ++i){cloud_in->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);cloud_in->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);cloud_in->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);}std::cout << "Saved " << cloud_in->points.size () << " data points to input:"<< std::endl;for (size_t i = 0; i < cloud_in->points.size (); ++i) std::cout << "    " <<cloud_in->points[i].x << " " << cloud_in->points[i].y << " " <<cloud_in->points[i].z << std::endl;*cloud_out = *cloud_in;std::cout << "size:" << cloud_out->points.size() << std::endl;for (size_t i = 0; i < cloud_in->points.size (); ++i)cloud_out->points[i].x = cloud_in->points[i].x + 0.7f;std::cout << "Transformed " << cloud_in->points.size () << " data points:"<< std::endl;for (size_t i = 0; i < cloud_out->points.size (); ++i)std::cout << "    " << cloud_out->points[i].x << " " <<cloud_out->points[i].y << " " << cloud_out->points[i].z << std::endl;pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;icp.setInputCloud(cloud_in);icp.setInputTarget(cloud_out);pcl::PointCloud<pcl::PointXYZ> Final;icp.align(Final);std::cout << "has converged:" << icp.hasConverged() << " score: " <<icp.getFitnessScore() << std::endl;std::cout << icp.getFinalTransformation() << std::endl;return (0);
}

#说明
现在，让我们逐个分解这个代码。

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

这些头文件包含我们将使用的所有类的定义。

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in (new pcl::PointCloud<pcl::PointXYZ>);pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZ>);

创建两个pcl::PointCloud<pcl::PointXYZ> boost shared pointers并初始化它们。每个点的类型在pcl命名空间中设置为PointXYZ，即：

// \brief A point structure representing Euclidean xyz coordinates.
struct PointXYZ
{float x;float y;float z;
};

The lines:

  // Fill in the CloudIn datacloud_in->width    = 5;cloud_in->height  = 1;cloud_in->is_dense = false;cloud_in->points.resize (cloud_in->width * cloud_in->height);for (size_t i = 0; i < cloud_in->points.size (); ++i){cloud_in->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);cloud_in->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);cloud_in->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);}std::cout << "Saved " << cloud_in->points.size () << " data points to input:"<< std::endl;for (size_t i = 0; i < cloud_in->points.size (); ++i) std::cout << "    " <<cloud_in->points[i].x << " " << cloud_in->points[i].y << " " <<cloud_in->points[i].z << std::endl;*cloud_out = *cloud_in;std::cout << "size:" << cloud_out->points.size() << std::endl;

用随机点值填充PointCloud结构，并设置合适的参数（宽度，高度，is_dense）。此外，他们输出保存的点数，以及它们的实际数据值。

然后：

  for (size_t i = 0; i < cloud_in->points.size (); ++i)cloud_out->points[i].x = cloud_in->points[i].x + 0.7f;std::cout << "Transformed " << cloud_in->points.size () << " data points:"<< std::endl;for (size_t i = 0; i < cloud_out->points.size (); ++i)std::cout << "    " << cloud_out->points[i].x << " " <<cloud_out->points[i].y << " " << cloud_out->points[i].z << std::endl;

对点云执行简单的刚性变换并再次输出数据值。

  pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;icp.setInputCloud(cloud_in);icp.setInputTarget(cloud_out);

这将创建一个IterativeClosestPoint的实例并为其提供一些有用的信息。“icp.setInputCloud（cloud_in）;”将cloud_in设置为PointCloud开始，“icp.setInputTarget（cloud_out）;which we want cloud_in to look like.

接下来，

  pcl::PointCloud<pcl::PointXYZ> Final;icp.align(Final);std::cout << "has converged:" << icp.hasConverged() << " score: " <<icp.getFitnessScore() << std::endl;std::cout << icp.getFinalTransformation() << std::endl;

创建一个pcl::PointCloud<pcl::PointXYZ>，IterativeClosestPoint可以在应用该算法后将其保存到生成的云中。如果两个PointCloud正确对齐（意味着它们都是同一个云，而且只是某种刚性转换应用于其中一个云），则icp.hasConverged() = 1 (true)。It then outputs the fitness score of the final transformation and some information about it.

#编译和运行程序
将以下行添加到您的CMakeLists.txt文件中：

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

After you have made the executable, you can run it. Simply do:

./iterative_closest_point

You will see something similar to:

Saved 5 data points to input:
0.352222 -0.151883 -0.106395
-0.397406 -0.473106 0.292602
-0.731898 0.667105 0.441304
-0.734766 0.854581 -0.0361733
-0.4607 -0.277468 -0.916762
size:5
Transformed 5 data points:
1.05222 -0.151883 -0.106395
0.302594 -0.473106 0.292602
-0.0318983 0.667105 0.441304
-0.0347655 0.854581 -0.03617330.2393 -0.277468 -0.916762
[pcl::SampleConsensusModelRegistration::setInputCloud] Estimated a sample
selection distance threshold of: 0.200928
[pcl::IterativeClosestPoint::computeTransformation] Number of
correspondences 4 [80.000000%] out of 5 points [100.0%], RANSAC rejected:
1 [20.000000%].
[pcl::IterativeClosestPoint::computeTransformation] Convergence reached.
Number of iterations: 1 out of 0. Transformation difference: 0.700001
has converged:1 score: 1.95122e-141  4.47035e-08 -3.25963e-09          0.7
2.98023e-08            1 -1.08499e-07 -2.98023e-08
1.30385e-08 -1.67638e-08            1  1.86265e-080            0            0            1

How to use iterative closest point