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欧式聚类实现方法大致是：

1、找到空间中某点 $p_1$，用KD-Tree找到离他最近的n个点，判断这n个点到$p_1$的距离。将距离小于阈值r的点$p_2、p_3、p_4$…放在类Q里

2、在 $Q(p_1)$里找到一点$p_2$ ,重复步骤1

3、在$Q(p_1,p_2)$找到一点，重复步骤1，找到 $p_{22}、p_{23}$… 全部放进Q里

4、当Q再也不能有新点加入了，则完成搜索了

使用pcl库的欧式聚类：

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();
}

pcl实现原理：

 pcl::extractEuclideanClusters (const PointCloud<PointT> &cloud,const typename search::Search<PointT>::Ptr &tree,float tolerance, std::vector<PointIndices> &clusters,unsigned int min_pts_per_cluster,unsigned int max_pts_per_cluster)
{if (tree->getInputCloud ()->points.size () != cloud.points.size ())   // 点数量检查{PCL_ERROR ("[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n", tree->getInputCloud ()->points.size (), cloud.points.size ());return;}int nn_start_idx = tree->getSortedResults () ? 1 : 0;std::vector<bool> processed (cloud.points.size (), false);std::vector<int> nn_indices;std::vector<float> nn_distances; for (int i = 0; i < static_cast<int> (cloud.points.size ()); ++i)   //遍历点云中的每一个点{if (processed[i])continue;std::vector<int> seed_queue;int sq_idx = 0;seed_queue.push_back (i); processed[i] = true;while (sq_idx < static_cast<int> (seed_queue.size ()))    //遍历每一个种子{if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))  {sq_idx++;continue;}for (size_t j = nn_start_idx; j < nn_indices.size (); ++j)    {if (nn_indices[j] == -1 || processed[nn_indices[j]]) continue; seed_queue.push_back (nn_indices[j]); processed[nn_indices[j]] = true;}sq_idx++;}if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster){pcl::PointIndices r;r.indices.resize (seed_queue.size ());for (size_t j = 0; j < seed_queue.size (); ++j)r.indices[j] = seed_queue[j];std::sort (r.indices.begin (), r.indices.end ());r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());r.header = cloud.header;clusters.push_back (r);}}
}