基于合力的平面点云边缘提取代码

以下是基于合力的平面点云边缘提取的示例代码：

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/features/integral_image_normal.h>

int main(int argc, char** argv)
{
  // Load input point cloud
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
  pcl::io::loadPCDFile("input_cloud.pcd", *cloud);

  // Compute normals for all points
  pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
  ne.setInputCloud(cloud);
  pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());
  ne.setSearchMethod(tree);
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>);
  ne.setRadiusSearch(0.03);
  ne.compute(*cloud_normals);

  // Compute integral image normal for all points
  pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne_integral;
  ne_integral.setNormalEstimationMethod(ne_integral.AVERAGE_3D_GRADIENT);
  ne_integral.setMaxDepthChangeFactor(0.02f);
  ne_integral.setNormalSmoothingSize(10.0f);
  ne_integral.setInputCloud(cloud);
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals_integral(new pcl::PointCloud<pcl::Normal>);
  ne_integral.compute(*cloud_normals_integral);

  // Compute curvature for all points
  pcl::PointCloud<pcl::PointXYZINormal>::Ptr cloud_curvature(new pcl::PointCloud<pcl::PointXYZINormal>);
  pcl::concatenateFields(*cloud, *cloud_normals_integral, *cloud_curvature);
  pcl::search::KdTree<pcl::PointXYZINormal>::Ptr tree_curvature(new pcl::search::KdTree<pcl::PointXYZINormal>);
  tree_curvature->setInputCloud(cloud_curvature);
  pcl::CurvatureEstimation<pcl::PointXYZINormal, pcl::PointXYZINormal> ce;
  ce.setInputCloud(cloud_curvature);
  ce.setSearchMethod(tree_curvature);
  ce.setRadiusSearch(0.03);
  ce.compute(*cloud_curvature);

  // Compute forces for all points
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_forces(new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointXYZ force;
  for (int i = 0; i < cloud->size(); ++i)
  {
    pcl::PointXYZ& p = cloud->points[i];
    pcl::Normal& n = cloud_normals_integral->points[i];
    pcl::PointXYZINormal& c = cloud_curvature->points[i];

    force.x = n.normal_x * c.curvature;
    force.y = n.normal_y * c.curvature;
    force.z = n.normal_z * c.curvature;

    cloud_forces->push_back(force);
  }

  // Extract edge points based on forces
  pcl::ExtractIndices<pcl::PointXYZ> extractor;
  pcl::IndicesPtr indices(new std::vector<int>);
  for (int i = 0; i < cloud->size(); ++i)
  {
    pcl::PointXYZ& p = cloud->points[i];
    pcl::PointXYZ& f = cloud_forces->points[i];

    if (f.norm() > 0.1)
      indices->push_back(i);
  }

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_edges(new pcl::PointCloud<pcl::PointXYZ>);
  extractor.setInputCloud(cloud);
  extractor.setIndices(indices);
  extractor.filter(*cloud_edges);

  // Save edge points to file
  pcl::io::savePCDFile("output_edges.pcd", *cloud_edges);

  return 0;
}

这段代码中，我们首先加载了一个点云输入文件，然后计算了每个点的法向量和曲率。接着，我们通过计算每个点的合力来判断它是否为边缘点。最后，我们提取出所有的边缘点，并将它们保存到一个输出文件中。需要注意的是，这段代码中只处理了平面点云，如果要处理曲面点云，还需要进行额外的处理。