SLAM OpenCV提取和匹配ORB特征

这个程序使用OpenCV提取和匹配图像的ORB特征。输入图片是两张不同视角拍摄的同一物体程序运行后先提取出第一张图片的ORB特征按任意键(比如空格)之后显示直接匹配ORB特征的结果可以看到有一些线和大多数线不平行出现了匹配错误。再按任意键之后过滤掉了匹配错误的连线代码#include iostream #include opencv2/core/core.hpp #include opencv2/features2d/features2d.hpp #include opencv2/highgui/highgui.hpp #include chrono using namespace std; using namespace cv; int main(int argc, char **argv) { if (argc ! 3) { cout usage: feature_extraction img1 img2 endl; return 1; } //-- 读取图像 Mat img_1 imread(argv[1], CV_LOAD_IMAGE_COLOR); Mat img_2 imread(argv[2], CV_LOAD_IMAGE_COLOR); assert(img_1.data ! nullptr img_2.data ! nullptr); //-- 初始化 std::vectorKeyPoint keypoints_1, keypoints_2; Mat descriptors_1, descriptors_2; PtrFeatureDetector detector ORB::create(); PtrDescriptorExtractor descriptor ORB::create(); PtrDescriptorMatcher matcher DescriptorMatcher::create(BruteForce-Hamming); //-- 第一步:检测 Oriented FAST 角点位置 chrono::steady_clock::time_point t1 chrono::steady_clock::now(); detector-detect(img_1, keypoints_1); detector-detect(img_2, keypoints_2); //-- 第二步:根据角点位置计算 BRIEF 描述子 descriptor-compute(img_1, keypoints_1, descriptors_1); descriptor-compute(img_2, keypoints_2, descriptors_2); chrono::steady_clock::time_point t2 chrono::steady_clock::now(); chrono::durationdouble time_used chrono::duration_castchrono::durationdouble(t2 - t1); cout extract ORB cost time_used.count() seconds. endl; Mat outimg1; drawKeypoints(img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT); imshow(ORB features, outimg1); //-- 第三步:对两幅图像中的BRIEF描述子进行匹配使用 Hamming 距离 vectorDMatch matches; t1 chrono::steady_clock::now(); matcher-match(descriptors_1, descriptors_2, matches); t2 chrono::steady_clock::now(); time_used chrono::duration_castchrono::durationdouble(t2 - t1); cout match ORB cost time_used.count() seconds. endl; //-- 第四步:匹配点对筛选 // 计算最小距离和最大距离 auto min_max minmax_element(matches.begin(), matches.end(), [](const DMatch m1, const DMatch m2) { return m1.distance m2.distance; }); double min_dist min_max.first-distance; double max_dist min_max.second-distance; printf(-- Max dist : %f \n, max_dist); printf(-- Min dist : %f \n, min_dist); //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限. std::vectorDMatch good_matches; for (int i 0; i descriptors_1.rows; i) { if (matches[i].distance max(2 * min_dist, 30.0)) { good_matches.push_back(matches[i]); } } //-- 第五步:绘制匹配结果 Mat img_match; Mat img_goodmatch; drawMatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_match); drawMatches(img_1, keypoints_1, img_2, keypoints_2, good_matches, img_goodmatch); imshow(all matches, img_match); imshow(good matches, img_goodmatch); waitKey(0); return 0; }参考高翔《视觉SLAM十四讲》