视觉SLAM建图简单代码
时间: 2023-07-09 16:31:18 浏览: 45
以下是一个简单的视觉SLAM建图的代码示例,使用了OpenCV和PCL库:
```
#include <iostream>
#include <fstream>
#include <chrono>
#include <thread>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <pcl/common/common.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/pcl_visualizer.h>
using namespace std;
// Camera intrinsic parameters
const double fx = 718.856;
const double fy = 718.856;
const double cx = 607.1928;
const double cy = 185.2157;
const int width = 1241;
const int height = 376;
// Feature detector and descriptor
cv::Ptr<cv::FeatureDetector> detector = cv::ORB::create();
cv::Ptr<cv::DescriptorExtractor> descriptor = cv::ORB::create();
// Feature matching
cv::Ptr<cv::DescriptorMatcher> matcher = cv::DescriptorMatcher::create("BruteForce-Hamming");
// PCL point cloud
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGB>);
// Function for triangulating points and adding them to the point cloud
void triangulatePoints(const vector<cv::KeyPoint>& keypoints1, const vector<cv::KeyPoint>& keypoints2,
const vector<cv::DMatch>& matches, const cv::Mat& R, const cv::Mat& t) {
// Triangulate points
vector<cv::Point3d> points3d;
for (int i = 0; i < matches.size(); i++) {
cv::Point2d pt1 = keypoints1[matches[i].queryIdx].pt;
cv::Point2d pt2 = keypoints2[matches[i].trainIdx].pt;
cv::Mat pt1_homogeneous = (cv::Mat_<double>(3,1) << pt1.x, pt1.y, 1);
cv::Mat pt2_homogeneous = (cv::Mat_<double>(3,1) << pt2.x, pt2.y, 1);
cv::Mat pt1_normalized = pt1_homogeneous / fx;
cv::Mat pt2_normalized = pt2_homogeneous / fx;
cv::Mat A(4,3,CV_64F);
A.row(0) = pt1_normalized.at<double>(0) * R.row(2) - R.row(0);
A.row(1) = pt1_normalized.at<double>(1) * R.row(2) - R.row(1);
A.row(2) = pt2_normalized.at<double>(0) * R.row(2) - R.row(0);
A.row(3) = pt2_normalized.at<double>(1) * R.row(2) - R.row(1);
cv::Mat w, u, vt;
cv::SVD::compute(A, w, u, vt, cv::SVD::MODIFY_A + cv::SVD::FULL_UV);
cv::Mat point3d_homogeneous = vt.row(3).t();
cv::Point3d point3d(point3d_homogeneous.at<double>(0)/point3d_homogeneous.at<double>(3),
point3d_homogeneous.at<double>(1)/point3d_homogeneous.at<double>(3),
point3d_homogeneous.at<double>(2)/point3d_homogeneous.at<double>(3));
points3d.push_back(point3d);
}
// Add points to point cloud
for (int i = 0; i < points3d.size(); i++) {
pcl::PointXYZRGB point;
point.x = points3d[i].x;
point.y = points3d[i].y;
point.z = points3d[i].z;
point.r = 255;
point.g = 255;
point.b = 255;
cloud->push_back(point);
}
}
int main(int argc, char** argv) {
// Open video file
cv::VideoCapture cap("data/kitti_00.mp4");
if (!cap.isOpened()) {
cout << "Error opening video file" << endl;
return -1;
}
// Create PCL visualizer
pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("PointCloud Viewer"));
// Loop through frames
cv::Mat previmg, prevdescriptors;
vector<cv::KeyPoint> prevkeypoints;
for (int i = 0; i < 100; i++) {
cout << "Processing frame " << i << endl;
// Read frame
cv::Mat img;
cap >> img;
if (img.empty()) {
break;
}
cv::imshow("Camera Feed", img);
cv::waitKey(1);
// Detect keypoints and compute descriptors
vector<cv::KeyPoint> keypoints;
cv::Mat descriptors;
detector->detectAndCompute(img, cv::Mat(), keypoints, descriptors);
// If this is the first frame, store keypoints and descriptors and skip to next frame
if (i == 0) {
previmg = img;
prevkeypoints = keypoints;
prevdescriptors = descriptors;
continue;
}
// Match features between previous and current frames
vector<cv::DMatch> matches;
matcher->match(prevdescriptors, descriptors, matches);
// Filter matches by distance
double max_dist = 0;
double min_dist = 100;
for (int j = 0; j < matches.size(); j++) {
double dist = matches[j].distance;
if (dist < min_dist) {
min_dist = dist;
}
if (dist > max_dist) {
max_dist = dist;
}
}
vector<cv::DMatch> good_matches;
for (int j = 0; j < matches.size(); j++) {
if (matches[j].distance < 2*min_dist) {
good_matches.push_back(matches[j]);
}
}
// Draw matches
cv::Mat img_matches;
cv::drawMatches(previmg, prevkeypoints, img, keypoints, good_matches, img_matches);
cv::imshow("Matches", img_matches);
cv::waitKey(1);
// Estimate camera motion using essential matrix
vector<cv::Point2d> prevpoints, currpoints;
for (int j = 0; j < good_matches.size(); j++) {
prevpoints.push_back(prevkeypoints[good_matches[j].queryIdx].pt);
currpoints.push_back(keypoints[good_matches[j].trainIdx].pt);
}
cv::Mat E = cv::findEssentialMat(currpoints, prevpoints, fx, cv::Point2d(cx, cy), cv::RANSAC);
cv::Mat R, t;
cv::recoverPose(E, currpoints, prevpoints, R, t, fx, cv::Point2d(cx, cy));
// Triangulate points and add to point cloud
triangulatePoints(prevkeypoints, keypoints, good_matches, R, t);
// Visualize point cloud
viewer->removePointCloud("cloud");
viewer->addPointCloud(cloud, "cloud");
viewer->spinOnce();
std::this_thread::sleep_for(std::chrono::milliseconds(10));
// Store current frame as previous frame
previmg = img;
prevkeypoints = keypoints;
prevdescriptors = descriptors;
}
// Save point cloud to PCD file
pcl::io::savePCDFileBinary("output.pcd", *cloud);
cout << "Point cloud saved to output.pcd" << endl;
return 0;
}
```
请注意,这只是一个简单的示例代码,不包含完整的SLAM算法和优化过程,仅用于演示视觉SLAM的基本原理和实现方法。