PCL 使用ConditionalRemoval和RadiusOutlierRemoval移除离群点

一、算法原理

RadiusOutlierRemoval

它的滤波思想非常直接，就是在点云数据中，设定每个点一定半径范围内周围至少有足够多的近邻，不满足就会被删除。比如你指定了一个半径d，然后指定该半径内至少有1个邻居，那么下图中只有黄色的点将从点云中删除。如果指定了半径内至少有2个邻居，那么黄色和绿色的点都将从点云中删除。

ConditionalRemoval滤波器
它可以一次删除满足对输入的点云设定的一个或多个条件指标的所有数据点。
相比之下，RadiusOutlierRemoval滤波器非常适合去除单个的离群点。而ConditionalRemoval比较灵活，可以根据设置的条件进行过滤，有点像直通滤波。

二、代码实现

#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 的滤波器---------------
    //build the filter
    pcl::ConditionAnd<pcl::PointXYZ>::Ptr range_cond(new pcl::ConditionAnd<pcl::PointXYZ>);//实例化条件指针
    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);//保存原有点云结结构就是点的数目没有减少，采用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++赋值兼容规则。派生类对象可以用来初始化基类的引用

    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、结果展示

依次为，原数据、RadiusOutlierRemoval滤波、ConditionalRemoval滤波

四、官网链接pcl::ConditionalRemoval< PointT >

pcl::RadiusOutlierRemoval< PointT >

五、参考博客

PCL使用ConditionalRemoval和RadiusOutlierRemoval移除离群点
PCL点云滤波去噪