图像的特征点可以简单的理解为图像中比较显著显著的点，如轮廓点，较暗区域中的亮点，较亮区域中的暗点等。

ORB的全称是ORiented Brief，采用FAST（features from accelerated segment test）算法来检测特征点。

与Brisk，AKAZE类似，ORB也分两部分，即特征点提取和特征点描述。特征提取是由FAST（Features from Accelerated Segment Test）算法发展来的，它基于特征点周围的图像灰度值，检测候选特征点周围一圈的像素值，如果候选点周围领域内有足够多的像素点与该候选点的灰度值差别够大，则认为该候选点为一个特征点。而特征点描述是根据BRIEF（Binary Robust Independent Elementary Features）特征描述算法改进的。

将FAST特征点的检测方法与BRIEF特征描述子结合起来，并在它们原来的基础上做了改进与优化。据说ORB算法的速度是sift的100倍，是surf的10倍。
ORB算法是为解决BRIEF的缺陷而改进的，主要解决两个缺点：噪声敏感、旋转不变性。
关于具体的理论部分可以参考下面两篇文章：
参考文章：https://blog.csdn.net/gaotihong/article/details/78712017
参考文章：https://blog.csdn.net/guoyunfei20/article/details/78792770

代码部分：

#include<opencv2/opencv.hpp>
#include<iostream>
#include<math.h>

using namespace cv;
using namespace std;


Mat img1, img2;
void ORB_demo(int, void*);
int main(int argc, char** argv)
{
	 img1 = imread("D:/test/box.png");
	 img2 = imread("D:/test/box_in_scene.png");
	if (!img1.data|| !img2.data)
	{
		cout << "图片未找到！" << endl;
		return -1;
	}
	namedWindow("ORB_demo",CV_WINDOW_AUTOSIZE);
	
	ORB_demo(0,0);
	
	imshow("input image of box",img1);
	imshow("input image of box_in_scene", img2);

	
	waitKey(0);
	return 0;


}

/*---------------检测与匹配--------------*/
void ORB_demo(int, void *)
{
	int Hession = 400;
	double t1 = getTickCount();
	//特征点提取
	Ptr<ORB> detector = ORB::create(400);
	vector<KeyPoint> keypoints_obj;
	vector<KeyPoint> keypoints_scene;
	//定义描述子
	Mat descriptor_obj, descriptor_scene;
	//检测并计算成描述子
	detector->detectAndCompute(img1, Mat(), keypoints_obj, descriptor_obj);
	detector->detectAndCompute(img2, Mat(), keypoints_scene, descriptor_scene);

	double t2 = getTickCount();
	double t = (t2 - t1) * 1000 / getTickFrequency();
	//特征匹配
	FlannBasedMatcher fbmatcher(new flann::LshIndexParams(20, 10, 2));
	vector<DMatch> matches;
	//将找到的描述子进行匹配并存入matches中
	fbmatcher.match(descriptor_obj, descriptor_scene, matches);

	double minDist = 1000;
	double maxDist = 0;
	//找出最优描述子
	vector<DMatch> goodmatches;
	for (int i = 0; i < descriptor_obj.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist < minDist)
		{
			minDist=dist ;
		}
		if (dist > maxDist)
		{
			maxDist=dist;
		}

	}
	for (int i = 0; i < descriptor_obj.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist < max(2 * minDist, 0.02))
		{
			goodmatches.push_back(matches[i]);
		}
	}
	Mat orbImg;

	drawMatches(img1, keypoints_obj, img2, keypoints_scene, goodmatches, orbImg,
		Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

	//----------目标物体用矩形标识出来------------
	vector<Point2f> obj;
	vector<Point2f>scene;
	for (size_t i = 0; i < goodmatches.size(); i++)
	{
		obj.push_back(keypoints_obj[goodmatches[i].queryIdx].pt);
		scene.push_back(keypoints_scene[goodmatches[i].trainIdx].pt);
	}
	vector<Point2f> obj_corner(4);
	vector<Point2f> scene_corner(4);
	//生成透视矩阵
	Mat H = findHomography(obj, scene, RANSAC);

	obj_corner[0] = Point(0, 0);
	obj_corner[1] = Point(img1.cols, 0);
	obj_corner[2] = Point(img1.cols, img1.rows);
	obj_corner[3] = Point(0, img1.rows);
	//透视变换
	perspectiveTransform(obj_corner, scene_corner, H);
	Mat resultImg=orbImg.clone();
	

	for (int i = 0; i < 4; i++)
	{
		line(resultImg, scene_corner[i]+ Point2f(img1.cols, 0), scene_corner[(i + 1) % 4]+ Point2f(img1.cols, 0), Scalar(0, 0, 255), 2, 8, 0);
	}
	imshow("result image",resultImg);

	


	cout << "ORB执行时间为:" << t << "ms" << endl;
	cout << "最小距离为：" <<minDist<< endl;
	cout << "最大距离为：" << maxDist << endl;
	imshow("ORB_demo", orbImg);
}

这里呢，我也把ORB的特征点检测与描述子计算的执行时间打印出来了，在1000ms左右，也就是1s左右。如下图所示：

检测与匹配的结果：