pcl点云合并_pcl点云重建

//为提取点云时使用的搜素对象利用输入点云cloud_filtered创建Kd树对象tree。

pcl::search::KdTree::Ptr tree (new pcl::search::KdTree);
tree->setInputCloud (cloud_filtered);//创建点云索引向量，用于存储实际的点云信息

std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction ec;
ec.setClusterTolerance (0.02); //设置近邻搜索的搜索半径为2cm
ec.setMinClusterSize (100);//设置一个聚类需要的最少点数目为100
ec.setMaxClusterSize (25000); //设置一个聚类需要的最大点数目为25000
ec.setSearchMethod (tree);//设置点云的搜索机制
ec.setInputCloud (cloud_filtered);
ec.extract (cluster_indices);//从点云中提取聚类，并将点云索引保存在cluster_indices中

//迭代访问点云索引cluster_indices，直到分割出所有聚类

int j = 0;

for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
    {
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
    //创建新的点云数据集cloud_cluster，将所有当前聚类写入到点云数据集中
    for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); ++pit)
    {
        cloud_cluster->points.push_back (cloud_filtered->points[*pit]);
        cloud_cluster->width = cloud_cluster->points.size ();
        cloud_cluster->height = 1;
        cloud_cluster->is_dense = true; 
    }
    pcl::visualization::CloudViewer viewer("Cloud Viewer");
    viewer.showCloud(cloud_cluster);
    pause();
    }
保存每个cloud_cluster为单独的.pcd文件,一个文件就是一个聚类
}
}