lec10_2_normal_corner.cpp

//
// Tutorial Author: shapelim@kaist.ac.kr (임형태)
#include <pcl/point_types.h>
#include <pcl/features/normal_3d.h>

int main(int argc, char **argv) {
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointXYZ pt;

    pt.x =  0.00; pt.y =  0.00; pt.z = 0; cloud->points.push_back(pt);
    pt.x =  0.01; pt.y =  0.00; pt.z = 0; cloud->points.push_back(pt);
    pt.x =  0.00; pt.y =  0.01; pt.z = 0; cloud->points.push_back(pt);
    pt.x = -0.01; pt.y =  0.00; pt.z = 0; cloud->points.push_back(pt);
    pt.x =  0.00; pt.y = -0.01; pt.z = 0; cloud->points.push_back(pt);
    pt.x = 10.00; pt.y = 10.00; pt.z = 0; cloud->points.push_back(pt);

    /**
     * Main
     */
    // Create the normal estimation class, and pass the input dataset to it
    pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
    ne.setInputCloud(cloud);

    // Create an empty kdtree representation, and pass it to the normal estimation object.
    // Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());
    ne.setSearchMethod(tree);

    // Output datasets
    pcl::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>);

    // Use all neighbors in a sphere of radius 3cm
    ne.setRadiusSearch(0.03);

    ne.compute(*cloud_normals);

    /** print */
    for (auto const &pt: cloud_normals->points) {
        std::cout << pt << std::endl;
    }
    return 0;
}