opencv与ORB SLAM提取orb特征点比较

     在ORB SLAM中，通过四叉树的方式存储关键点，使得图像上特征点分布均匀，便于追踪。此次实验分别用opencv接口和ORB SLAM实现特征点的提取，效果图如下：
实现程序：

#include <iostream>
//#include <opencv2/core/core.hpp>
//#include <opencv2/features2d/features2d.hpp>
//#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
#include "ORBextractor.h"
using namespace std;
using namespace cv;

int main ( int argc, char** argv )
{
    //-- 读取图像
    Mat img_1 = imread ( "1.png" );
    Mat mImGray=img_1;
    Mat outimg1,outimg2;//输出图像
    cvtColor(mImGray,mImGray,CV_RGB2GRAY);//转换为灰度图

    //opencv中接口函数
    std::vector<KeyPoint> keypoints_1,keypoints_2;
    Mat descriptors_1,descriptors_2;
    Ptr<FeatureDetector> detector = ORB::create();
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    detector->detect ( mImGray,keypoints_1 );
    descriptor->compute ( mImGray, keypoints_1, descriptors_1 );
    drawKeypoints( img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
    imshow("opencv提取ORB特征点",outimg1);

    //调用ORB SLAM中特征提取函数
    ORBextractor* mpIniORBextractor;
    mpIniORBextractor = new ORBextractor(500,1.2,8,20,10);
    (*mpIniORBextractor)(mImGray,cv::Mat(),keypoints_2,descriptors_2 ) ;
    drawKeypoints( img_1, keypoints_2, outimg2, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
    imshow("ORB SLAM提取ORB特征点",outimg2);
    waitKey(0);
    return 0;
}

CMakeList.txt:

cmake_minimum_required(VERSION 3.13)
project(keypoint111)

set(CMAKE_CXX_STANDARD 11)
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
aux_source_directory(. DIR_SRCS)#当有多个源文件时，此命令查找当前目录下的所有源文件
#注意.后面有一个空格，将所有源文件的名称保存到DIR_SRCS中
add_executable(keypoint111 ${DIR_SRCS})
target_link_libraries(keypoint111 ${OpenCV_LIBS})

注意：CMake会将当前源文件的文件名赋值给DIR_SRCS,再指示该变量中的源文件需要编译成一个名为keypoint11的可执行文件。
ORB SLAM中特征提取程序分析：