使用c++写一个pcl1.8.1可以运行的三维最小生成树算法

以下是一个使用PCL 1.8.1实现三维最小生成树算法的示例代码：

#include <iostream>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/PCLPointCloud2.h>
#include <pcl/common/distances.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/search/kdtree.h>
#include <pcl/common/common.h>
#include <pcl/surface/mls.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/registration/icp.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/surface/gp3.h>
#include <pcl/io/vtk_io.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/search/flann_search.h>
#include <pcl/common/centroid.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/features/moment_of_inertia_estimation.h>
#include <pcl/common/transforms.h>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/conditional_euclidean_clustering.h>
#include <pcl/kdtree/kdtree_flann.h>

using namespace std;
using namespace pcl;

typedef PointXYZ PointType;
typedef PointCloud<PointType> PointCloudType;

struct Edge
{
    int start;
    int end;
    double cost;
};

struct Node
{
    int parent;
    int rank;
};

bool compareEdge(Edge e1, Edge e2)
{
    return e1.cost < e2.cost;
}

void MakeSet(Node* nodes, int size)
{
    for (int i = 0; i < size; i++)
    {
        nodes[i].parent = i;
        nodes[i].rank = 0;
    }
}

int FindSet(Node* nodes, int x)
{
    if (nodes[x].parent != x)
    {
        nodes[x].parent = FindSet(nodes, nodes[x].parent);
    }
    return nodes[x].parent;
}

void Union(Node* nodes, int x, int y)
{
    int xRoot = FindSet(nodes, x);
    int yRoot = FindSet(nodes, y);
    if (xRoot == yRoot)
        return;
    if (nodes[xRoot].rank < nodes[yRoot].rank)
    {
        nodes[xRoot].parent = yRoot;
    }
    else if (nodes[xRoot].rank > nodes[yRoot].rank)
    {
        nodes[yRoot].parent = xRoot;
    }
    else
    {
        nodes[yRoot].parent = xRoot;
        nodes[xRoot].rank++;
    }
}

vector<Edge> MinimumSpanningTree(PointCloudType::Ptr cloud)
{
    int size = cloud->size();
    vector<Node> nodes(size);
    vector<Edge> edges(size*size);

    for (int i = 0; i < size; i++)
    {
        for (int j = i + 1; j < size; j++)
        {
            double distance = euclideanDistance(cloud->points[i], cloud->points[j]);
            Edge edge;
            edge.start = i;
            edge.end = j;
            edge.cost = distance;
            edges.push_back(edge);
        }
    }

    sort(edges.begin(), edges.end(), compareEdge);
    MakeSet(&nodes[0], size);

    vector<Edge> result;
    int count = 0;
    for (int i = 0; i < edges.size(); i++)
    {
        if (count == size - 1)
            break;
        Edge edge = edges[i];
        int start = edge.start;
        int end = edge.end;
        if (FindSet(&nodes[0], start) != FindSet(&nodes[0], end))
        {
            Union(&nodes[0], start, end);
            result.push_back(edge);
            count++;
        }
    }

    return result;
}

int main(int argc, char** argv)
{
    if (argc < 2)
    {
        cout << "Usage: ./pcl_mst [input_file]" << endl;
        return -1;
    }

    PointCloudType::Ptr input_cloud(new PointCloudType);
    if (io::loadPCDFile(argv[1], *input_cloud) == -1)
    {
        cout << "Error reading file " << argv[1] << endl;
        return -1;
    }

    vector<Edge> mst = MinimumSpanningTree(input_cloud);

    PointCloudType::Ptr output_cloud(new PointCloudType);
    for (int i = 0; i < mst.size(); i++)
    {
        int start = mst[i].start;
        int end = mst[i].end;
        output_cloud->push_back(input_cloud->points[start]);
        output_cloud->push_back(input_cloud->points[end]);
    }

    visualization::PCLVisualizer viewer("Minimum Spanning Tree");
    viewer.addPointCloud(input_cloud, "input_cloud");
    viewer.setPointCloudRenderingProperties(visualization::PCL_VISUALIZER_POINT_SIZE, 3, "input_cloud");
    viewer.addPointCloud(output_cloud, "output_cloud");
    viewer.setPointCloudRenderingProperties(visualization::PCL_VISUALIZER_COLOR, 1, 0, 0, "output_cloud");
    viewer.setPointCloudRenderingProperties(visualization::PCL_VISUALIZER_LINE_WIDTH, 3, "output_cloud");

    while (!viewer.wasStopped())
    {
        viewer.spinOnce();
    }

    return 0;
}

该代码实现了一个简单的Prim算法，用于计算三维点云的最小生成树。它首先计算所有点之间的距离，并将它们存储在一个边列表中。然后，按照边的成本对列表进行排序，以便按顺序处理它们。在处理每个边时，它检查该边的两个端点是否在同一集合中。如果不在同一集合中，则将它们合并，并将边添加到输出中。最后，它显示了原始点云和最小生成树的可视化效果。

注意，这只是一个简单的实现，它没有考虑优化技巧，如使用Fibonacci堆来管理边列表。