PCLの円柱当てはめ結果の参照方法が知りたい

PointCloudLibraryによるCylinder model segmentationによって、点群の円柱パラメータを表示することができました。次にこの7個のパラメータをプログラム内で参照し、別の変数に代入するなどしたいのですが、やり方がわかりません。
どのようにコーディングすれば、各パラメータの参照が可能となるでしょうか。よろしくお願い致します。

サンプルプログラム

C++
1#include <pcl/ModelCoefficients.h>
2#include <pcl/io/pcd_io.h>
3#include <pcl/point_types.h>
4#include <pcl/filters/extract_indices.h>
5#include <pcl/filters/passthrough.h>
6#include <pcl/features/normal_3d.h>
7#include <pcl/sample_consensus/method_types.h>
8#include <pcl/sample_consensus/model_types.h>
9#include <pcl/segmentation/sac_segmentation.h>
10
11typedef pcl::PointXYZ PointT;
12
13int
14main (int argc, char** argv)
15{
16  // All the objects needed
17  pcl::PCDReader reader;
18  pcl::PassThrough<PointT> pass;
19  pcl::NormalEstimation<PointT, pcl::Normal> ne;
20  pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg; 
21  pcl::PCDWriter writer;
22  pcl::ExtractIndices<PointT> extract;
23  pcl::ExtractIndices<pcl::Normal> extract_normals;
24  pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ());
25
26  // Datasets
27  pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
28  pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>);
29  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
30  pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<PointT>);
31  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);
32  pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_cylinder (new pcl::ModelCoefficients);
33  pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_cylinder (new pcl::PointIndices);
34
35  // Read in the cloud data
36  reader.read ("table_scene_mug_stereo_textured.pcd", *cloud);
37  std::cerr << "PointCloud has: " << cloud->size () << " data points." << std::endl;
38
39  // Build a passthrough filter to remove spurious NaNs
40  pass.setInputCloud (cloud);
41  pass.setFilterFieldName ("z");
42  pass.setFilterLimits (0, 1.5);
43  pass.filter (*cloud_filtered);
44  std::cerr << "PointCloud after filtering has: " << cloud_filtered->size () << " data points." << std::endl;
45
46  // Estimate point normals
47  ne.setSearchMethod (tree);
48  ne.setInputCloud (cloud_filtered);
49  ne.setKSearch (50);
50  ne.compute (*cloud_normals);
51
52  // Create the segmentation object for the planar model and set all the parameters
53  seg.setOptimizeCoefficients (true);
54  seg.setModelType (pcl::SACMODEL_NORMAL_PLANE);
55  seg.setNormalDistanceWeight (0.1);
56  seg.setMethodType (pcl::SAC_RANSAC);
57  seg.setMaxIterations (100);
58  seg.setDistanceThreshold (0.03);
59  seg.setInputCloud (cloud_filtered);
60  seg.setInputNormals (cloud_normals);
61  // Obtain the plane inliers and coefficients
62  seg.segment (*inliers_plane, *coefficients_plane);
63  std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;
64
65  // Extract the planar inliers from the input cloud
66  extract.setInputCloud (cloud_filtered);
67  extract.setIndices (inliers_plane);
68  extract.setNegative (false);
69
70  // Write the planar inliers to disk
71  pcl::PointCloud<PointT>::Ptr cloud_plane (new pcl::PointCloud<PointT> ());
72  extract.filter (*cloud_plane);
73  std::cerr << "PointCloud representing the planar component: " << cloud_plane->size () << " data points." << std::endl;
74  writer.write ("table_scene_mug_stereo_textured_plane.pcd", *cloud_plane, false);
75
76  // Remove the planar inliers, extract the rest
77  extract.setNegative (true);
78  extract.filter (*cloud_filtered2);
79  extract_normals.setNegative (true);
80  extract_normals.setInputCloud (cloud_normals);
81  extract_normals.setIndices (inliers_plane);
82  extract_normals.filter (*cloud_normals2);
83
84  // Create the segmentation object for cylinder segmentation and set all the parameters
85  seg.setOptimizeCoefficients (true);
86  seg.setModelType (pcl::SACMODEL_CYLINDER);
87  seg.setMethodType (pcl::SAC_RANSAC);
88  seg.setNormalDistanceWeight (0.1);
89  seg.setMaxIterations (10000);
90  seg.setDistanceThreshold (0.05);
91  seg.setRadiusLimits (0, 0.1);
92  seg.setInputCloud (cloud_filtered2);
93  seg.setInputNormals (cloud_normals2);
94
95  // Obtain the cylinder inliers and coefficients
96  seg.segment (*inliers_cylinder, *coefficients_cylinder);
97  std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;
98
99  // Write the cylinder inliers to disk
100  extract.setInputCloud (cloud_filtered2);
101  extract.setIndices (inliers_cylinder);
102  extract.setNegative (false);
103  pcl::PointCloud<PointT>::Ptr cloud_cylinder (new pcl::PointCloud<PointT> ());
104  extract.filter (*cloud_cylinder);
105  if (cloud_cylinder->points.empty ()) 
106    std::cerr << "Can't find the cylindrical component." << std::endl;
107  else
108  {
109	  std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->size () << " data points." << std::endl;
110	  writer.write ("table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false);
111  }
112  return (0);
113}

円柱当てはめ結果

[pcl::SACSegmentationFromNormals::initSACModel] Using a model of type: SACMODEL_CYLINDER
[pcl::SACSegmentationFromNormals::initSACModel] Setting radius limits to 0.000000/0.100000
[pcl::SACSegmentationFromNormals::initSACModel] Setting normal distance weight to 0.100000
[pcl::SACSegmentationFromNormals::initSAC] Using a method of type: SAC_RANSAC with a model threshold of 0.050000
[pcl::SampleConsensusModelCylinder::optimizeModelCoefficients] LM solver finished with exit code 2, having a residual norm of 0.322616.
Initial solution: 0.0452105 0.0924601 0.790215 0.20495 -0.721649 -0.661225 0.0422902
Final solution: 0.0452105 0.0924601 0.790215 0.20495 -0.721649 -0.661225 0.0396354
Cylinder coefficients: header:
  seq: 0
  stamp: 0.000000000
  frame_id:
values[]
  values[0]: 0.0452105
  values[1]: 0.0924601
  values[2]: 0.790215
  values[3]: 0.20495
  values[4]: -0.721649
  values[5]: -0.661225
  values[6]: 0.0396354

PointCloud representing the cylindrical component: 8625 data points.

行動規範の内容に同意します

回答1件

ベストアンサー

pcl::ModelCoefficients が values というメンバ変数 ( 型は std::vector<float> ) を持っており、出力に表示されているのはその内容のようです。
ですので coefficients_cylinder->values をコピーするなりそのまま使うなりするのが良いのではないでしょうか。

API ドキュメントの該当箇所へのリンクも併せて置いておきます。

投稿2021/01/19 02:04

編集2021/01/19 02:08