与上篇笔记一样https://blog.csdn.net/yamgyutou/article/details/105080379，本篇介绍算子都可用于关键点检测，都属于低层次的点云处理。

为什么特征检测以及算子的详细介绍：https://www.cnblogs.com/AndyJee/p/3734805.html

以下为百度百科介绍：

Harris算子：https://baike.baidu.com/item/Harris%20%E7%AE%97%E5%AD%90/22251459?fr=aladdin

sift算子：https://baike.baidu.com/item/SIFT/1396275?fr=aladdin

Harris角点具有旋转、光照不变性，但不具有尺度不变性。SIFT具有尺度不变性。

Harris算子：该算法耗时比较长

参考：https://www.cnblogs.com/ironstark/p/5064848.html

https://blog.csdn.net/yudingjun0611/article/details/7991601

#include <iostream>
#include <pcl\io\pcd_io.h>
#include <pcl/point_cloud.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/io/io.h>
#include <pcl/keypoints/harris_3D.h>
#include <cstdlib>
#include <vector>
using namespace std;

int
main()
{
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
	pcl::io::loadPCDFile ("roorm.pcd", *cloud);

		//注意Harris的输出点云必须是有强度(I)信息的，因为评估值保存在I分量里
	pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZI>);
	pcl::HarrisKeypoint3D<pcl::PointXYZ, pcl::PointXYZI, pcl::Normal>* harris = new pcl::HarrisKeypoint3D<pcl::PointXYZ, pcl::PointXYZI, pcl::Normal>;

	harris->setInputCloud(cloud);
	cout<<"input successful"<<endl;
	harris->setNonMaxSupression(true);
	harris->setRadius(0.04f);
	harris->setThreshold(0.02f);
	cout<<"parameter set successful"<<endl;
		//新建的点云必须初始化，清零，否则指针会越界
	cloud_out->height=1;
	cloud_out->width =100;
	cloud_out->resize(cloud_out->height*cloud->width);
		cloud_out->clear();
	harris->compute(*cloud_out);
	int size = cloud_out->size();

	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_harris (new pcl::PointCloud<pcl::PointXYZ>);
	cloud_harris->height=1;
	cloud_harris->width =100;
	cloud_harris->resize(cloud_out->height*cloud->width);
	cloud_harris->clear();

	pcl::PointXYZ point;
		//可视化结果不支持XYZI格式点云，所有又要导回XYZ格式。。。。
	for (int i = 0;i<size;i++)
	{
		point.x = cloud_out->at(i).x;
		point.y = cloud_out->at(i).y;
		point.z = cloud_out->at(i).z;
		cloud_harris->push_back(point);
	}

	boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer);
	
	pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud_color_handler(cloud, 255, 0, 0); 
	viewer->addPointCloud(cloud, cloud_color_handler,"all_cloud");
	pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> harris_color_handler (cloud_harris, 0, 255, 0);
	viewer->addPointCloud(cloud_harris,harris_color_handler,"harris");
	viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "harris");

	while (!viewer->wasStopped())
	{
		viewer->spinOnce();

	}
	system("pause");
}

sift算子：

#include <iostream>
#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 = 0.005f;//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;

}