随手笔记------根据相机位姿实现多个RGBD图像点云的叠加
说明
程序主要实现:(1). 根据内参计算一对 RGB-D 图像对应的点云;(2). 根据各张图的相机位姿(也就是外参),把点云加起来,组成地图。
源代码
#include <iostream>
#include <fstream>
#include <opencv2/opencv.hpp>
#include <boost/format.hpp> // for formating strings
#include <pangolin/pangolin.h>
#include <sophus/se3.hpp>
using namespace std;
typedef vector<Sophus::SE3d, Eigen::aligned_allocator<Sophus::SE3d>> TrajectoryType;
typedef Eigen::Matrix<double, 6, 1> Vector6d;
// 在pangolin中画图,已写好,无需调整
void showPointCloud(
const vector<Vector6d, Eigen::aligned_allocator<Vector6d>> &pointcloud);
int main(int argc, char **argv) {
vector<cv::Mat> colorImgs, depthImgs; // 彩色图和深度图
TrajectoryType poses; // 相机位姿
ifstream fin("./pose.txt");
if (!fin) {
cerr << "请在有pose.txt的目录下运行此程序" << endl;
return 1;
}
for (int i = 0; i < 5; i++) {
boost::format fmt("./%s/%d.%s"); //图像文件格式
colorImgs.push_back(cv::imread((fmt % "color" % (i + 1) % "png").str()));
depthImgs.push_back(cv::imread((fmt % "depth" % (i + 1) % "pgm").str(), -1)); // 使用-1读取原始图像
double data[7] = {0};
for (auto &d:data)
fin >> d;
Sophus::SE3d pose(Eigen::Quaterniond(data[6], data[3], data[4], data[5]),
Eigen::Vector3d(data[0], data[1], data[2]));
poses.push_back(pose);
}
// 计算点云并拼接
// 相机内参
double cx = 325.5;
double cy = 253.5;
double fx = 518.0;
double fy = 519.0;
double depthScale = 1000.0;
vector<Vector6d, Eigen::aligned_allocator<Vector6d>> pointcloud;
pointcloud.reserve(1000000);
for (int i = 0; i < 5; i++) {
cout << "转换图像中: " << i + 1 << endl;
cv::Mat color = colorImgs[i];
cv::Mat depth = depthImgs[i];
Sophus::SE3d T = poses[i];
for (int v = 0; v < color.rows; v++)
for (int u = 0; u < color.cols; u++) {
unsigned int d = depth.ptr<unsigned short>(v)[u]; // 深度值
if (d == 0) continue; // 为0表示没有测量到
Eigen::Vector3d point;
point[2] = double(d) / depthScale;
point[0] = (u - cx) * point[2] / fx;
point[1] = (v - cy) * point[2] / fy;
Eigen::Vector3d pointWorld = T * point;
Vector6d p;
p.head<3>() = pointWorld;
p[5] = color.data[v * color.step + u * color.channels()]; // blue
p[4] = color.data[v * color.step + u * color.channels() + 1]; // green
p[3] = color.data[v * color.step + u * color.channels() + 2]; // red
pointcloud.push_back(p);
}
}
cout << "点云共有" << pointcloud.size() << "个点." << endl;
showPointCloud(pointcloud);
return 0;
}
void showPointCloud(const vector<Vector6d, Eigen::aligned_allocator<Vector6d>> &pointcloud) {
if (pointcloud.empty()) {
cerr << "Point cloud is empty!" << endl;
return;
}
pangolin::CreateWindowAndBind("Point Cloud Viewer", 1024, 768);
glEnable(GL_DEPTH_TEST);
glEnable(GL_BLEND);
glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);
pangolin::OpenGlRenderState s_cam(
pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000),
pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0)
);
pangolin::View &d_cam = pangolin::CreateDisplay()
.SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f)
.SetHandler(new pangolin::Handler3D(s_cam));
while (pangolin::ShouldQuit() == false) {
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
d_cam.Activate(s_cam);
glClearColor(1.0f, 1.0f, 1.0f, 1.0f);
glPointSize(2);
glBegin(GL_POINTS);
for (auto &p: pointcloud) {
glColor3d(p[3] / 255.0, p[4] / 255.0, p[5] / 255.0);
glVertex3d(p[0], p[1], p[2]);
}
glEnd();
pangolin::FinishFrame();
usleep(5000); // sleep 5 ms
}
return;
}
CMakeLists.txt
find_package(Sophus REQUIRED)
include_directories(${Sophus_INCLUDE_DIRS})
find_package(Pangolin REQUIRED)
add_executable(joinMap joinMap.cpp)
target_link_libraries(joinMap ${OpenCV_LIBS} ${Pangolin_LIBRARIES})