用C++写一个点云配准程序

好的，点云配准是一个广泛应用于三维重建、机器人视觉、自动驾驶等领域的基本问题。下面是一个简单的点云配准程序的C++实现，采用了ICP算法：

#include <iostream>
#include <fstream>
#include <cmath>
#include <Eigen/Core>
#include <Eigen/Dense>
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h>

using namespace std;
using namespace Eigen;

const int MAX_ITERATIONS = 50;
const double THRESHOLD = 1e-8;

// 点云配准函数
void pointCloudRegistration(const pcl::PointCloud<pcl::PointXYZ>::Ptr& sourceCloud, const pcl::PointCloud<pcl::PointXYZ>::Ptr& targetCloud, Matrix4d& transform)
{
    int n = sourceCloud->size();
    MatrixXd sourceMat(3, n), targetMat(3, n);
    for(int i=0; i<n; ++i)
    {
        sourceMat(0, i) = sourceCloud->points[i].x;
        sourceMat(1, i) = sourceCloud->points[i].y;
        sourceMat(2, i) = sourceCloud->points[i].z;

        targetMat(0, i) = targetCloud->points[i].x;
        targetMat(1, i) = targetCloud->points[i].y;
        targetMat(2, i) = targetCloud->points[i].z;
    }

    // 初始化变换矩阵
    transform = Matrix4d::Identity();

    for(int iter=0; iter<MAX_ITERATIONS; ++iter)
    {
        // 计算最近邻点
        MatrixXd sourceMatTransformed = transform * sourceMat;
        vector<int> indices(n);
        vector<float> sqrDistances(n);
        pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;
        kdtree.setInputCloud(targetCloud);
        for(int i=0; i<n; ++i)
        {
            pcl::PointXYZ sourcePoint(sourceMatTransformed(0, i), sourceMatTransformed(1, i), sourceMatTransformed(2, i));
            kdtree.nearestKSearch(sourcePoint, 1, indices[i], sqrDistances[i]);
        }

        // 计算变换矩阵
        MatrixXd A(3, 3);
        VectorXd b(3);
        A.setZero();
        b.setZero();
        double mse = 0.0;
        for(int i=0; i<n; ++i)
        {
            if(sqrDistances[i] < THRESHOLD)
            {
                Vector3d sourcePoint(sourceMat(0, i), sourceMat(1, i), sourceMat(2, i));
                Vector3d targetPoint(targetMat(0, indices[i]), targetMat(1, indices[i]), targetMat(2, indices[i]));
                mse += (sourcePoint - targetPoint).squaredNorm();

                A += sourcePoint * targetPoint.transpose();
                b += targetPoint;
            }
        }

        if(mse/n < THRESHOLD)
        {
            break;
        }

        Vector3d t = A.colPivHouseholderQr().solve(b);
        Matrix4d T = Matrix4d::Identity();
        T.block<3, 1>(0, 3) = t;
        transform = T * transform;
    }
}

int main(int argc, char** argv)
{
    if(argc < 3)
    {
        cerr << "Usage: " << argv[0] << " source.ply target.ply" << endl;
        return -1;
    }

    pcl::PointCloud<pcl::PointXYZ>::Ptr sourceCloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PLYReader reader;
    reader.read(argv[1], *sourceCloud);

    pcl::PointCloud<pcl::PointXYZ>::Ptr targetCloud(new pcl::PointCloud<pcl::PointXYZ>);
    reader.read(argv[2], *targetCloud);

    Matrix4d transform;
    pointCloudRegistration(sourceCloud, targetCloud, transform);
    cout << "Transform Matrix:" << endl << transform << endl;

    return 0;
}

这个程序使用了Eigen库进行矩阵计算，使用了PCL库进行点云读取和最近邻搜索。在主函数中，首先读取了两个点云文件，然后调用pointCloudRegistration函数进行点云配准，最后输出变换矩阵。pointCloudRegistration函数的实现分为两个主要步骤：计算最近邻点和计算变换矩阵。在计算最近邻点时，使用了KdTreeFLANN实现最近邻搜索，可以快速地找到每个源点对应的最近邻点。在计算变换矩阵时，使用了ICP算法，不断迭代计算变换矩阵，直到满足收敛条件为止。