视觉SLAM--回环检测

回环检测的意义：可以使 后端位姿图得到一个 全局一致估计。
视觉SLAM的主流做法： 基于外观的回环检测方法，仅 根据两幅图像的相似性确定回环检测关系。这种方法，摆脱了累计误差，使得回环检测模块可以称为SLAM系统中相对独立的模块。

创建字典

词袋，Bag-of-Words(BoW),目的是用"图像上有哪几种特征"来描述一幅图像。

ch11\feature_training.cpp

int main( int argc, char** argv ) {
    // read the image 
    cout<<"reading images... "<<endl;
    vector<Mat> images; 
    for ( int i=0; i<10; i++ )
    {
        string path = "./data/"+to_string(i+1)+".png";
        images.push_back( imread(path) );
    }
    // detect ORB features
    cout<<"detecting ORB features ... "<<endl;
    Ptr< Feature2D > detector = ORB::create();
    vector<Mat> descriptors;
    for ( Mat& image:images )
    {
        vector<KeyPoint> keypoints; 
        Mat descriptor;
        detector->detectAndCompute( image, Mat(), keypoints, descriptor );
        descriptors.push_back( descriptor );
    }
    
    // create vocabulary 
    cout<<"creating vocabulary ... "<<endl;
    DBoW3::Vocabulary vocab;
    vocab.create( descriptors );
    cout<<"vocabulary info: "<<vocab<<endl;
    vocab.save( "vocabulary.yml.gz" );
    cout<<"done"<<endl;
    
    return 0;
}

相似度计算

ch11\loop_closure.cpp

int main(int argc, char **argv) {
    // read the images and database  
    cout << "reading database" << endl;
    DBoW3::Vocabulary vocab("./vocabulary.yml.gz");
    // DBoW3::Vocabulary vocab("./vocab_larger.yml.gz");  // use large vocab if you want: 
    if (vocab.empty()) {
        cerr << "Vocabulary does not exist." << endl;
        return 1;
    }
    cout << "reading images... " << endl;
    vector<Mat> 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 lead to overfit.
    // detect ORB features
    cout << "detecting ORB features ... " << endl;
    Ptr<Feature2D> detector = ORB::create();
    vector<Mat> descriptors;
    for (Mat &image:images) {
        vector<KeyPoint> keypoints;
        Mat descriptor;
        detector->detectAndCompute(image, Mat(), keypoints, descriptor);
        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 " << endl;
    for (int i = 0; i < images.size(); i++) {
        DBoW3::BowVector v1;
        vocab.transform(descriptors[i], v1);
        for (int j = i; j < images.size(); j++) {
            DBoW3::BowVector v2;
            vocab.transform(descriptors[j], v2);
            double score = vocab.score(v1, v2);
            cout << "image " << i << " vs image " << j << " : " << score << endl;
        }
        cout << endl;
    }

    // or with database 
    cout << "comparing images with database " << endl;
    DBoW3::Database db(vocab, false, 0);
    for (int i = 0; i < descriptors.size(); i++)
        db.add(descriptors[i]);
    cout << "database info: " << db << endl;
    for (int i = 0; i < descriptors.size(); i++) {
        DBoW3::QueryResults ret;
        db.query(descriptors[i], ret, 4);      // max result=4
        cout << "searching for image " << i << " returns " << ret << endl << endl;
    }
    cout << "done." << endl;
}

增加字典规模

ch11\gen_vocab_large.cpp

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!"<<endl;
        return 1;
    }

    vector<string> rgb_files, depth_files;
    vector<double> 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 ... "<<endl;
    vector<Mat> descriptors;
    Ptr< Feature2D > detector = ORB::create();
    int index = 1;
    for ( string rgb_file:rgb_files )
    {
        Mat image = imread(rgb_file);
        vector<KeyPoint> keypoints; 
        Mat descriptor;
        detector->detectAndCompute( image, Mat(), keypoints, descriptor );
        descriptors.push_back( descriptor );
        cout<<"extracting features from image " << index++ <<endl;
    }
    cout<<"extract total "<<descriptors.size()*500<<" features."<<endl;
    
    // create vocabulary 
    cout<<"creating vocabulary, please wait ... "<<endl;
    DBoW3::Vocabulary vocab;
    vocab.create( descriptors );
    cout<<"vocabulary info: "<<vocab<<endl;
    vocab.save( "vocab_larger.yml.gz" );
    cout<<"done"<<endl;
    
    return 0;
}

参考资料：
1、书籍：《视觉SLAM十四讲：从理论到实践（第2版）》
2、代码：https://github.com/gaoxiang12/slambook2

原文地址：https://blog.csdn.net/xiner0114/article/details/140452052

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