slam特征点深度 svd_GCNv2-SLAM：用CNN提取特征点取代ORB

感谢您的问题。SLAM中的特征点深度是指相机到地图中的特征点的距离。svd_GCNv2-SLAM是一种使用CNN提取特征点的SLAM方法，它使用图卷积神经网络（GCN）来从图像中提取特征点，然后使用SVD（奇异值分解）来估算它们的深度，以建立地图。与传统基于手工设计的ORB特征点的方法相比，使用CNN提取特征点可以更好地捕捉图像中的结构和纹理信息，从而提高了SLAM性能。

相关问题

给入两个ORBSLAM运动轨迹txt，使轨迹对齐输出轨迹txt文件代码C++

以下是一个简单的C++代码示例，可以将两个ORBSLAM运动轨迹文件对齐，并将结果输出到新的轨迹文件中。代码中使用了Eigen库来进行矩阵计算。

#include <iostream>
#include <fstream>
#include <Eigen/Dense>

using namespace Eigen;

// 定义一个结构体来存储轨迹中的每个位姿信息
struct Pose
{
    double timestamp;
    Vector3d position;
    Quaterniond orientation;
};

// 从文件中读取轨迹数据
bool readTrajectory(const std::string& filename, std::vector<Pose>& trajectory)
{
    std::ifstream file(filename);
    if (!file.is_open())
    {
        std::cerr << "Error: Cannot open file " << filename << std::endl;
        return false;
    }

    while (!file.eof())
    {
        Pose pose;
        file >> pose.timestamp >> pose.position.x() >> pose.position.y() >> pose.position.z() >> pose.orientation.w() >> pose.orientation.x() >> pose.orientation.y() >> pose.orientation.z();
        if (file.good()) {
            trajectory.push_back(pose);
        }
    }

    file.close();
    return true;
}

// 将对齐后的轨迹数据写入文件
bool writeTrajectory(const std::string& filename, const std::vector<Pose>& trajectory)
{
    std::ofstream file(filename);
    if (!file.is_open())
    {
        std::cerr << "Error: Cannot open file " << filename << std::endl;
        return false;
    }

    for (const auto& pose : trajectory)
    {
        file << pose.timestamp << " " << pose.position.x() << " " << pose.position.y() << " " << pose.position.z() << " " << pose.orientation.w() << " " << pose.orientation.x() << " " << pose.orientation.y() << " " << pose.orientation.z() << std::endl;
    }

    file.close();
    return true;
}

int main(int argc, char** argv)
{
    if (argc != 4)
    {
        std::cerr << "Usage: align_trajectory <trajectory1.txt> <trajectory2.txt> <output.txt>" << std::endl;
        return -1;
    }

    // 读取两个轨迹文件
    std::vector<Pose> trajectory1, trajectory2;
    if (!readTrajectory(argv[1], trajectory1) || !readTrajectory(argv[2], trajectory2))
    {
        return -1;
    }

    // 计算两个轨迹的平移和旋转矩阵
    Matrix3d R;
    Vector3d t;
    int N = std::min(trajectory1.size(), trajectory2.size());
    MatrixXd P1(3, N), P2(3, N);
    for (int i = 0; i < N; i++)
    {
        P1.col(i) = trajectory1[i].position;
        P2.col(i) = trajectory2[i].position;
    }
    Vector3d centroid1 = P1.rowwise().mean();
    Vector3d centroid2 = P2.rowwise().mean();
    P1.colwise() -= centroid1;
    P2.colwise() -= centroid2;
    Matrix3d H = P1 * P2.transpose();
    JacobiSVD<MatrixXd> svd(H, ComputeFullU | ComputeFullV);
    Matrix3d U = svd.matrixU();
    Matrix3d V = svd.matrixV();
    R = V * U.transpose();
    if (R.determinant() < 0) {
        R.col(2) *= -1;
    }
    t = centroid2 - R * centroid1;

    // 对齐第二个轨迹并输出
    std::vector<Pose> aligned_trajectory;
    for (const auto& pose : trajectory2)
    {
        Pose aligned_pose;
        aligned_pose.timestamp = pose.timestamp;
        aligned_pose.position = R * pose.position + t;
        aligned_pose.orientation = R * pose.orientation;
        aligned_trajectory.push_back(aligned_pose);
    }
    if (!writeTrajectory(argv[3], aligned_trajectory))
    {
        return -1;
    }

    std::cout << "Done!" << std::endl;
    return 0;
}

使用方法：将以上代码保存为`align_trajectory.cpp`，并使用以下命令进行编译：

g++ align_trajectory.cpp -o align_trajectory -std=c++11 -I /path/to/eigen

其中，`/path/to/eigen`是Eigen库的路径，需要根据实际情况进行修改。编译成功后，可以使用以下命令运行程序：

./align_trajectory trajectory1.txt trajectory2.txt output.txt

其中，`trajectory1.txt`和`trajectory2.txt`分别是要对齐的两个轨迹文件，`output.txt`是输出的对齐后的轨迹文件。

视觉SLAM建图简单代码

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。