PCL_点云分割_渐进式形态学滤波地面分割_渐进形态学滤波

在点云滤波中，可以基于模型进行平面、球、圆柱等进行分割，现采用渐进式形态学滤波进行地面分割。通过创建pcl::ProgressiveMorphologicalFilter滤波器对象和添加相关参数，如：增加滤波器的窗口大小和高差阈值将建筑、汽车和植被等非地面物体与地面进行分割。
代码：

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/segmentation/progressive_morphological_filter.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/console/parse.h>
int
main()
{
	int max_w_s(20);
	float slope(1.0f);
	float initial_d(0.5f);
	float max_d(3.0f);
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>), cloud_ground(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::PointIndicesPtr ground(new pcl::PointIndices);
	/*if(argc<2)
	{
	std::cout << "Usage: " << argv[0] << " filename.pcd [Options]" << std::endl << std::endl;
	std::cout << "Options:" << std::endl;
	std::cout << "     -mw(20):                     Max window size." << std::endl;
	std::cout << "     -s(1.0):                     Slope." << std::endl;
	std::cout << "     -id(0.5):                    initial distance." << std::endl;
	std::cout << "     -md(3.0):                     Max distance" << std::endl;
	exit(1);
	}*/
	// Fill in the cloud data
	pcl::PCDReader reader;
	// Replace the path below with the path where you saved your file
	reader.read<pcl::PointXYZ>("samp11-utm.pcd", *cloud);
	/*	pcl::console::parse_argument (argc, argv, "-mw", max_w_s);
	pcl::console::parse_argument (argc, argv, "-s", slope);
	pcl::console::parse_argument (argc, argv, "-id", initial_d);
	pcl::console::parse_argument (argc, argv, "-md", max_d);
	*/
	std::cerr << "Cloud before filtering: " << std::endl;
	std::cerr << *cloud << std::endl;

	// Create the filtering object
	pcl::ProgressiveMorphologicalFilter<pcl::PointXYZ> pmf;
	pmf.setInputCloud(cloud);
	pmf.setMaxWindowSize(max_w_s);
	pmf.setSlope(slope);
	pmf.setInitialDistance(initial_d);
	pmf.setMaxDistance(max_d);
	pmf.extract(ground->indices);

	// Create the filtering object
	pcl::ExtractIndices<pcl::PointXYZ> extract;
	extract.setInputCloud(cloud);
	extract.setIndices(ground);
	extract.filter(*cloud_ground);

	std::cerr << "Ground cloud after filtering: " << std::endl;
	std::cerr << *cloud_ground << std::endl;
	int v1, v2;
	pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("点云库PCL学习教程第二版-激光雷达点云地面提取"));

	viewer->createViewPort(0, 0, 0.5, 1, v1);
	viewer->createViewPort(0.5, 0, 1, 1, v2);
	viewer->setBackgroundColor(255, 255, 255, v1);
	viewer->setBackgroundColor(255, 255, 255, v2);
	pcl::PCDWriter writer;
	writer.write<pcl::PointXYZ>("samp11-utm_ground.pcd", *cloud_ground, false);
	viewer->addPointCloud(cloud_ground, pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>(cloud_ground, 0, 255, 0), "cloud_ground", v1);
	// Extract non-ground returns
	extract.setNegative(true);
	extract.filter(*cloud_filtered);
	viewer->addPointCloud(cloud_filtered, pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>(cloud_filtered, 0, 0, 255), "cloud_filtered", v2);
	viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 4, "cloud_ground", v1);
	viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 4, "cloud_filtered", v2);
	std::cerr << "Object cloud after filtering: " << std::endl;
	std::cerr << *cloud_filtered << std::endl;
	viewer->spin();
	writer.write<pcl::PointXYZ>("samp11-utm_object.pcd", *cloud_filtered, false);

	return (0);
}
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运行结果：