SLAM - 回环检测

feature_training.cpp

#include "DBoW3/DBoW3.h"//词袋所需要的头文件DBoW
#include 
#include 
#include 
#include 
#include 
#include 

using namespace cv;
using namespace std;

/***************************************************
 * 本节演示了如何根据data/目录下的十张图训练字典
 * ************************************************/

int main( int argc, char** argv )
{
    // read the image 
    cout<<"reading images... "< images; 
    for ( int i=0; i<10; i++ )//遍历读取十张图像放入Mat类的images对象中
    {
        string path = "./data/"+to_string(i+1)+".png";
        images.push_back( imread(path) );
    }
    // detect ORB features
    cout<<"detecting ORB features ... "< detector = ORB::create();
    vector descriptors;//描述子
    for ( Mat& image:images )//遍历images检测描述子
    {
        vector keypoints; 
        Mat descriptor;
        detector->detectAndCompute( image, Mat(), keypoints, descriptor );
        descriptors.push_back( descriptor );
    }
    
    // create vocabulary 
    cout<<"creating vocabulary ... "<

loop_closeure.cpp

#include "DBoW3/DBoW3.h"
#include 
#include 
#include 
#include 
#include 
#include 

using namespace cv;
using namespace std;

/***************************************************
 * 本节演示了如何根据前面训练的字典计算相似性评分
 * ************************************************/
int main( int argc, char** argv )
{
    // read the images and database  
    cout<<"reading database"< images; 
    for ( int i=0; i<10; i++ )
    {
        string path = "./data/"+to_string(i+1)+".png";
        images.push_back( imread(path) );
    }
    
    // NOTE: in this case we are comparing images with a vocabulary generated by themselves, this may leed to overfitting.  
    // detect ORB features
    cout<<"detecting ORB features ... "< detector = ORB::create();
    vector descriptors;
    for ( Mat& image:images )
    {
        vector keypoints; 
        Mat descriptor;
        detector->detectAndCompute( image, Mat(), keypoints, descriptor );//检测图像中的关键点和计算描述子
        descriptors.push_back( descriptor );
    }
    
    // we can compare the images directly or we can compare one image to a database 
    // images :
    cout<<"comparing images with images "<

gen_vocab_large.cpp

#include "DBoW3/DBoW3.h"
#include 
#include 
#include 
#include 
#include 
#include 

using namespace cv;
using namespace std;


int main( int argc, char** argv )
{
    string dataset_dir = argv[1];
    ifstream fin ( dataset_dir+"/associate.txt" );
    if ( !fin )
    {
        cout<<"please generate the associate file called associate.txt!"< rgb_files, depth_files;
    vector rgb_times, depth_times;
    while ( !fin.eof() )
    {
        string rgb_time, rgb_file, depth_time, depth_file;
        fin>>rgb_time>>rgb_file>>depth_time>>depth_file;
        rgb_times.push_back ( atof ( rgb_time.c_str() ) );
        depth_times.push_back ( atof ( depth_time.c_str() ) );
        rgb_files.push_back ( dataset_dir+"/"+rgb_file );
        depth_files.push_back ( dataset_dir+"/"+depth_file );

        if ( fin.good() == false )
            break;
    }
    fin.close();
    
    cout<<"generating features ... "< descriptors;
    Ptr< Feature2D > detector = ORB::create();
    int index = 1;
    for ( string rgb_file:rgb_files )
    {
        Mat image = imread(rgb_file);
        vector keypoints; 
        Mat descriptor;
        detector->detectAndCompute( image, Mat(), keypoints, descriptor );
        descriptors.push_back( descriptor );
        cout<<"extracting features from image " << index++ <

 https://blog.csdn.net/weixin_53660567/article/details/121750728https://blog.csdn.net/weixin_53660567/article/details/121750728