//統計濾波
//#include "pch.h"
//#include <iostream>
//#include <pcl/io/pcd_io.h>
//#include <pcl/point_types.h>
//#include <pcl/filters/statistical_outlier_removal.h> 
//int
//main (int argc, char** argv)
//{  
//	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); 
//	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);   // 定義讀取對象  
//	pcl::PCDReader reader;  // 讀取點云文件  
//	reader.read<pcl::PointXYZ> ("F://cout-saved//back_1.pcd", *cloud);   
//	std::cerr << "Cloud before filtering: " << std::endl;  
//	std::cerr << *cloud << std::endl;   
//	// 創建濾波器，對每個點分析的臨近點的個數設置為50 ，并將標準差的倍數設置為1  這意味著如果一   
//	//個點的距離超出了平均距離一個標準差以上，則該點被標記為離群點，并將它移除，存儲起來  
//	pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor;   //創建濾波器對象  
//	sor.setInputCloud (cloud);                            //設置待濾波的點云  
//	//sor.setMeanK (100);                               //設置在進行統計時考慮查詢點臨近點數  
//	//sor.setStddevMulThresh (1.0);   //back_1_inliers.pcd                   //設置判斷是否為離群點的閥值  
//
//	sor.setMeanK (250);                               //設置在進行統計時考慮查詢點臨近點數  
//	//sor.setStddevMulThresh (1.0); //2 
//	//sor.setStddevMulThresh(2.0);//3
//	//sor.setStddevMulThresh(0.6);//4
//	sor.setStddevMulThresh(0.3);//4
//	sor.filter (*cloud_filtered);                    //存儲   
//	std::cerr << "Cloud after filtering: " << std::endl; 
//	std::cerr << *cloud_filtered << std::endl;   
//	pcl::PCDWriter writer;  writer.write<pcl::PointXYZ> ("F://cout-saved//back_1_inliers5.pcd", *cloud_filtered, false);   
//	sor.setNegative (true);  
//	sor.filter (*cloud_filtered);  
//	writer.write<pcl::PointXYZ> ("F://cout-saved//back_1_outliers5.pcd", *cloud_filtered, false);   
//	return (0);
//}

StatisticalOutlierRemoval濾波器主要可以用來剔除離群點，或者測量誤差導致的粗差點。
濾波思想為：對每一個點的鄰域進行一個統計分析，計算它到所有臨近點的平均距離。假設得到的結果是一個高斯分布，其形狀是由均值和標準差決定，那么平均距離在標準范圍(由全局距離平均值和方差定義)之外的點，可以被定義為離群點并從數據中去除。

 // The arrays to be usedstd::vector<int> nn_indices (mean_k_);std::vector<float> nn_dists (mean_k_);std::vector<float> distances (indices_->size ());//存儲每個點的距離indices.resize (indices_->size ());removed_indices_->resize (indices_->size ());int oii = 0, rii = 0;  // oii = output indices iterator, rii = removed indices iterator

//第一 計算每個點到所有K鄰域點的平均距離。//First pass: Compute the mean distances for all points with respect to their k nearest neighborsint valid_distances = 0;for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii)  // iii = input indices iterator{if (!pcl_isfinite (input_->points[(*indices_)[iii]].x) ||!pcl_isfinite (input_->points[(*indices_)[iii]].y) ||!pcl_isfinite (input_->points[(*indices_)[iii]].z)){distances[iii] = 0.0;continue;}// Perform the nearest k searchif (searcher_->nearestKSearch ((*indices_)[iii], mean_k_ + 1, nn_indices, nn_dists) == 0){distances[iii] = 0.0;PCL_WARN ("[pcl::%s::applyFilter] Searching for the closest %d neighbors failed.\n", getClassName ().c_str (), mean_k_);continue;}// Calculate the mean distance to its neighborsdouble dist_sum = 0.0;for (int k = 1; k < mean_k_ + 1; ++k)  // k = 0 is the query point 查詢點dist_sum += sqrt (nn_dists[k]);distances[iii] = static_cast<float> (dist_sum / mean_k_);valid_distances++;}

//計算整個點集距離容器的平均值和樣本標準差//Estimate the mean and the standard deviation of the distance vectordouble sum = 0, sq_sum = 0;for (size_t i = 0; i < distances.size (); ++i){sum += distances[i];sq_sum += distances[i] * distances[i];}double mean = sum / static_cast<double>(valid_distances);  //距離平均值double variance = (sq_sum - sum * sum / static_cast<double>(valid_distances)) / (static_cast<double>(valid_distances) - 1);  //樣本方差double stddev = sqrt (variance);  //樣本標準差//getMeanStd (distances, mean, stddev);//距離閾值等于平均距離加上標準差倍數double distance_threshold = mean + std_mul_ * stddev;

//依次將距離閾值與每個點的distances[iii]比較 ，超出閾值的點被標記為離群點，并將其移除。// Second pass: Classify the points on the computed distance thresholdfor (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii)  // iii = input indices iterator{// Points having a too high average distance are outliers and are passed to removed indices// Unless negative was set, then it's the opposite conditionif ((!negative_ && distances[iii] > distance_threshold) || (negative_ && distances[iii] <= distance_threshold)){if (extract_removed_indices_)(*removed_indices_)[rii++] = (*indices_)[iii];continue;}// Otherwise it was a normal point for output (inlier)indices[oii++] = (*indices_)[iii];}// Resize the output arraysindices.resize (oii);removed_indices_->resize (rii);
}

結果顯示：

總結

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