3d激光雷达开发（sift关键点）

        关键点在数据匹配、三维重建和物体识别方面发挥了巨大的作用。和narf关键点相比较，sift点用的更多。不光是点云数据，sift最早是用在图像方面，相信有过图像开发经验的朋友应该不陌生。这个算法是受专利保护的算法，这一点需要注意下。

1、准备sift.cpp

// STL
#include <iostream>

// PCL
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/common/io.h>
#include <pcl/keypoints/sift_keypoint.h>
#include <pcl/features/normal_3d.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/console/time.h>

/* This examples shows how to estimate the SIFT points based on the
 * z gradient of the 3D points than using the Intensity gradient as
 * usually used for SIFT keypoint estimation.
 */

namespace pcl
{
  template<>
  struct SIFTKeypointFieldSelector<PointXYZ>
  {
    inline float
      operator () (const PointXYZ &p) const
    {
      return p.z;
    }
  };
}

int
main(int, char** argv)
{
  std::string filename = argv[1];
  std::cout << "Reading " << filename << std::endl;
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz(new pcl::PointCloud<pcl::PointXYZ>);
  if (pcl::io::loadPCDFile<pcl::PointXYZ>(filename, *cloud_xyz) == -1) // load the file
  {
    PCL_ERROR("Couldn't read file");
    return -1;
  }
  std::cout << "points: " << cloud_xyz->points.size() << std::endl;

  // Parameters for sift computation
  const float min_scale = 0.005f; //the standard deviation of the smallest scale in the scale space
  const int n_octaves = 6;//the number of octaves (i.e. doublings of scale) to compute
  const int n_scales_per_octave = 4;//the number of scales to compute within each octave
  const float min_contrast = 0.001f;//the minimum contrast required for detection


  pcl::console::TicToc time;
  time.tic();
  // Estimate the sift interest points using z values from xyz as the Intensity variants
  pcl::SIFTKeypoint<pcl::PointXYZ, pcl::PointWithScale> sift;
  pcl::PointCloud<pcl::PointWithScale> result;
  pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());
  sift.setSearchMethod(tree);
  sift.setScales(min_scale, n_octaves, n_scales_per_octave);
  sift.setMinimumContrast(min_contrast);
  sift.setInputCloud(cloud_xyz);
  sift.compute(result);
  std::cout << "Computing the SIFT points takes " << time.toc() / 1000 << "seconds" << std::endl;
  std::cout << "No of SIFT points in the result are " << result.points.size() << std::endl;


  // Copying the pointwithscale to pointxyz so as visualize the cloud
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_temp(new pcl::PointCloud<pcl::PointXYZ>);
  copyPointCloud(result, *cloud_temp);
  std::cout << "SIFT points in the result are " << cloud_temp->points.size() << std::endl;
  // Visualization of keypoints along with the original cloud
  pcl::visualization::PCLVisualizer viewer("PCL Viewer");
  pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> keypoints_color_handler(cloud_temp, 0, 255, 0);
  pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud_color_handler(cloud_xyz, 255, 0, 0);
  viewer.setBackgroundColor(0.0, 0.0, 0.0);
  viewer.addPointCloud(cloud_xyz, cloud_color_handler, "cloud");//add point cloud
  viewer.addPointCloud(cloud_temp, keypoints_color_handler, "keypoints");//add the keypoints
  viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "keypoints");

  while (!viewer.wasStopped())
  {
    viewer.spinOnce();
  }


  return 0;

}

2、准备CMakeLists.txt

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)

project(sift)

find_package(PCL 1.2 REQUIRED)

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

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

3、创建sln工程，准备编译

4、准备执行sift.exe

        为了查看效果，可以准备两个点云文件。比如，一个是bunny.pcd，一个是dragon.pcd。输入sift.exe bunny.pcd的效果如下所示，

        后者的效果如下所示，

         随着功能越来越多，相关的dll也要及时添加，不然很有可能执行不起来。