/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2014-, Open Perception, Inc.
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* modification, are permitted provided that the following conditions
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* * Redistributions in binary form must reproduce the above
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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#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/recognition/hv/hv_go.h>
#include <pcl/registration/icp.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>
typedef pcl::PointXYZRGBA PointType;
typedef pcl::Normal NormalType;
typedef pcl::ReferenceFrame RFType;
typedef pcl::SHOT352 DescriptorType;
struct CloudStyle
{
double r;
double g;
double b;
double size;
CloudStyle(double r,
double g,
double b,
double size) :
r(r),
g(g),
b(b),
size(size)
{
}
};
CloudStyle style_white(255.0, 255.0, 255.0, 4.0);
CloudStyle style_red(255.0, 0.0, 0.0, 3.0);
CloudStyle style_green(0.0, 255.0, 0.0, 5.0);
CloudStyle style_cyan(93.0, 200.0, 217.0, 4.0);
CloudStyle style_violet(255.0, 0.0, 255.0, 8.0);
std::string model_filename_;
std::string scene_filename_;
//Algorithm params
bool show_keypoints_(false);
bool use_hough_(true);
float model_ss_(0.02f);
float scene_ss_(0.02f);
float rf_rad_(0.015f);
float descr_rad_(0.02f);
float cg_size_(0.01f);
float cg_thresh_(5.0f);
int icp_max_iter_(5);
float icp_corr_distance_(0.005f);
float hv_resolution_(0.005f);
float hv_occupancy_grid_resolution_(0.01f);
float hv_clutter_reg_(5.0f);
float hv_inlier_th_(0.005f);
float hv_occlusion_th_(0.01f);
float hv_rad_clutter_(0.03f);
float hv_regularizer_(3.0f);
float hv_rad_normals_(0.05);
bool hv_detect_clutter_(true);
/**
* Prints out Help message
* @param filename Runnable App Name
*/
void
showHelp(char* filename)
{
std::cout << std::endl;
std::cout << "***************************************************************************" << std::endl;
std::cout << "* *" << std::endl;
std::cout << "* Global Hypothese Verification 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 keypoints." << std::endl;
std::cout << " --algorithm (Hough|GC): Clustering algorithm used (default Hough)." << std::endl;
std::cout << " --model_ss val: Model uniform sampling radius (default " << model_ss_ << ")" << std::endl;
std::cout << " --scene_ss val: Scene uniform sampling radius (default " << scene_ss_ << ")" << std::endl;
std::cout << " --rf_rad val: Reference frame radius (default " << rf_rad_ << ")" << std::endl;
std::cout << " --descr_rad val: Descriptor radius (default " << descr_rad_ << ")" << std::endl;
std::cout << " --cg_size val: Cluster size (default " << cg_size_ << ")" << std::endl;
std::cout << " --cg_thresh val: Clustering threshold (default " << cg_thresh_ << ")" << std::endl << std::endl;
std::cout << " --icp_max_iter val: ICP max iterations number (default " << icp_max_iter_ << ")" << std::endl;
std::cout << " --icp_corr_distance val: ICP correspondence distance (default " << icp_corr_distance_ << ")" << std::endl << std::endl;
std::cout << " --hv_clutter_reg val: Clutter Regularizer (default " << hv_clutter_reg_
PCL Hypothesis Verification 识别目标过滤 去除假阳性
最新推荐文章于 2025-08-05 01:00:10 发布
本文介绍了使用Point Cloud Library(PCL)进行目标识别的过程中,如何通过假设验证来有效过滤假阳性结果,提高识别准确性。

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