一、算法原理
RadiusOutlierRemoval
它的濾波思想非常直接,就是在點雲數據中,設定每個點一定半徑範圍內周圍至少有足夠多的近鄰,不滿足就會被刪除。比如你指定了一個半徑d,然後指定該半徑內至少有1個鄰居,那麼下圖中只有黃色的點將從點雲中刪除。如果指定了半徑內至少有2個鄰居,那麼黃色和綠色的點都將從點雲中刪除。
ConditionalRemoval濾波器
它可以一次刪除滿足對輸入的點雲設定的一個或多個條件指標的所有數據點。
相比之下,RadiusOutlierRemoval濾波器非常適合去除單個的離羣點。而ConditionalRemoval比較靈活,可以根據設置的條件進行過濾,有點像直通濾波。
二、代碼實現
#include <iostream>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/filters/conditional_removal.h>
#include <pcl/visualization/pcl_visualizer.h>
using namespace std;
int main(int argc, char** argv) {
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_conditional(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_radius(new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::loadPCDFile("666.pcd", *cloud_in);
//------------radius_remove-----------------
pcl::RadiusOutlierRemoval<pcl::PointXYZ> outrem;
outrem.setInputCloud(cloud_in);
outrem.setRadiusSearch(0.5);
outrem.setMinNeighborsInRadius(20);
outrem.filter(*cloud_radius);
//pcl::io::savePCDFileASCII("cloud_radius.pcd", *cloud_radius);
//--------------condition_remove 的濾波器---------------
//build the filter
pcl::ConditionAnd<pcl::PointXYZ>::Ptr range_cond(new pcl::ConditionAnd<pcl::PointXYZ>);//實例化條件指針
range_cond->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZ>("z", pcl::ComparisonOps::GT, 0.0)));
range_cond->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZ>("z", pcl::ComparisonOps::LT, 5.0)));
//build the filter
pcl::ConditionalRemoval<pcl::PointXYZ> condrem;
condrem.setCondition(range_cond);
condrem.setInputCloud(cloud_in);
condrem.setKeepOrganized(true);//保存原有點雲結結構就是點的數目沒有減少,採用nan代替了
//apply filter
condrem.filter(*cloud_conditional);
//pcl::io::savePCDFileASCII("cloud_conditional.pcd", *cloud_conditional);
cout << "cloud_in: " << cloud_in->size() << " points" << endl;
cout << "cloud_radius: " << cloud_radius->size() << " points" << endl;
cout << "cloud_conditional: " << cloud_conditional->size() << " points" << endl;
//visualizer
pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer);
//viewer->initCameraParameters();
int v1(0);
viewer->createViewPort(0, 0, 0.33, 1, v1);
viewer->setBackgroundColor(0, 255, 0, v1);
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> color1(cloud_in, 255, 0, 0);
viewer->addPointCloud(cloud_in, color1, "cloud_in", v1);//C++賦值兼容規則。派生類對象可以用來初始化基類的引用
int v2(0);
viewer->createViewPort(0.33, 0, 0.66, 1, v2);
viewer->setBackgroundColor(0, 0, 255, v2);
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> color2(cloud_radius, 255, 0, 0);
viewer->addPointCloud(cloud_radius, color2, "cloud_radius", v2);
int v3(0);
viewer->createViewPort(0.66, 0, 1, 1, v3);
viewer->setBackgroundColor(0, 255, 0, v3);
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> color3(cloud_conditional, 255, 0, 0);
viewer->addPointCloud(cloud_conditional, color3, "cloud_conditional", v3);
//viewer->addCoordinateSystem();
viewer->spin();
return 0;
}
三、結果展示
依次爲,原數據、RadiusOutlierRemoval濾波、ConditionalRemoval濾波