【记录】3D Object Recognition based on Correspondence Grouping

最新推荐文章于 2024-04-20 19:31:18 发布

ASHush

最新推荐文章于 2024-04-20 19:31:18 发布

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分类专栏： C++ PCL Recognition

本文链接：https://blog.youkuaiyun.com/weixin_43661854/article/details/95077556

本文档详细介绍了使用点云库PCL进行3D对象识别的过程，特别是侧重于通过对应组群策略来实现高精度识别的技术细节。

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// 教程官网：http://pointclouds.org/documentation/tutorials/correspondence_grouping.php#correspondence-grouping
// 添加计时
#include <pcl/io/pcd_io.h>
#include <pcl/point_cloud.h>
#include <pcl/correspondence.h>
#include <pcl/features/normal_3d_omp.h>
#include <pcl/features/shot_omp.h>
#include <pcl/features/board.h>
#include <pcl/filters/uniform_sampling.h>
#include <pcl/recognition/cg/hough_3d.h>
#include <pcl/recognition/cg/geometric_consistency.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/kdtree/impl/kdtree_flann.hpp>
#include <pcl/common/transforms.h>
#include <pcl/console/parse.h>
#include <pcl/common/time.h> // 计时头文件

typedef pcl::PointXYZRGBA PointType;
typedef pcl::Normal NormalType;
typedef pcl::ReferenceFrame RFType;
typedef pcl::SHOT352 DescriptorType;

std::string model_filename_;
std::string scene_filename_;

//Algorithm params
bool show_keypoints_(false);
bool show_correspondences_(false);
bool use_cloud_resolution_(false);
bool use_hough_(true);
float model_ss_(0.01f);
float scene_ss_(0.03f);
float rf_rad_(0.015f);
float descr_rad_(0.02f);
float cg_size_(0.01f);
float cg_thresh_(5.0f);

void
showHelp(char *filename) // 在控制台上打印程序可以接受的几个命令行开关的简短解释
{
	std::cout << std::endl;
	std::cout << "***************************************************************************" << std::endl;
	std::cout << "*                                                                         *" << std::endl;
	std::cout << "*             Correspondence Grouping Tutorial - Usage Guide              *" << std::endl;
	std::cout << "*                                                                         *" << std::endl;
	std::cout << "***************************************************************************" << std::endl << std::endl;
	std::cout << "Usage: " << filename << " model_filename.pcd scene_filename.pcd [Options]" << std::endl << std::endl;
	std::cout << "Options:" << std::endl;
	std::cout << "     -h:                     Show this help." << std::endl;
	std::cout << "     -k:                     Show used keypoints." << std::endl; // 可视化构造对应点时用到的关键点
	std::cout << "     -c:                     Show used correspondences." << std::endl; // 可视化支持实例假设的对应点对
	std::cout << "     -r:                     Compute the model cloud resolution and multiply" << std::endl;// 使用点云数据的分辨率
	std::cout << "                             each radius given by that value." << std::endl; // 设置后，其他参数值将会使用与点云分辨率相乘的结果，而非用户直接设置的值
	std::cout << "     --algorithm (Hough|GC): Clustering algorithm used (default Hough)." << std::endl; // 选择对应点的聚类方法
	std::cout << "     --model_ss val:         Model uniform sampling radius (default 0.01)" << std::endl; // 模型下采样半径
	std::cout << "     --scene_ss val:         Scene uniform sampling radius (default 0.03)" << std::endl; // 场景下采样半径
	std::cout << "     --rf_rad val:           Reference frame radius (default 0.015)" << std::endl; // 局部参考坐标系半径
	std::cout << "     --descr_rad val:        Descriptor radius (default 0.02)" << std::endl; // 特征描述子的半径
	std::cout << "     --cg_size val:          Cluster size (default 0.01)" << std::endl; // 对应Hough空间分辨率或GC空间分辨率
	std::cout << "     --cg_thresh val:        Clustering threshold (default 5)" << std::endl << std::endl; // 允许的最小聚类大小
}

void
parseCommandLine(int argc, char *argv[]) // 实际解析命令行参数，以便为执行设置正确的参数
{
	//Show help
	if (pcl::con