PCL功能匹配失败

Question

我试图通过更改许多参数来匹配我测试的两点云之间的特征，但是它总是产生错误的匹配。我正在计算SIFT功能的PFH功能描述符。

谢谢您的建议。

下面是我使用的代码

// load the both point clouds
pcl::io::loadPCDFile("Tee.pcd", *cloud_1);
pcl::PLYReader Reader;
Reader.read("tee.ply", *cloud_2);
//pcl::io::loadPCDFile("Tee.pcd", *cloud_2);

    // Create the filtering object
pcl::PassThrough<pcl::PointXYZRGB> pass;
pass.setInputCloud(cloud_2);
pass.setFilterFieldName("z");
pass.setFilterLimits(0.0, 1.0);
//pass.setFilterLimitsNegative (true);
pass.filter(*cloud_2_filtered);


// Downsample the cloud
const float voxel_grid_leaf_size = 0.009f;
downsample(cloud_1, voxel_grid_leaf_size, downsampledCloud_1);
std::cout << "First cloud: downsampled " << std::endl;
const float voxel_grid_leaf_size2 = 0.003f;
downsample(cloud_2_filtered, voxel_grid_leaf_size2, downsampledCloud_2);
std::cout << "second cloud: downsampled " << std::endl;

// Compute surface normals
const float normal_radius = 0.03;
compute_surface_normals(downsampledCloud_1, normal_radius, normalsFromCloud_1);
compute_surface_normals(downsampledCloud_2, normal_radius, normalsFromCloud_2);
std::cout << "second cloud: normals computed " << std::endl;
// Compute keypoints
const float min_scale = 0.01;
const int nr_octaves = 3;
const int nr_octaves_per_scale = 6;
const float min_contrast = 1.0;
detect_keypoints(cloud_1, min_scale, nr_octaves, nr_octaves_per_scale, min_contrast, keypointsFromCloud_1);
std::cout << "first cloud: keypoints computed " << std::endl;
//const float min_scale1 = 0.1;
detect_keypoints(cloud_2_filtered, min_scale, nr_octaves, nr_octaves_per_scale, min_contrast, keypointsFromCloud_2);
std::cout << "second cloud: keypoints computed " << std::endl;
//visualize_keypoints(cloud_2, keypointsFromCloud_2);

// Compute PFH features
const float feature_radius = 0.08;
compute_PFH_features_at_keypoints(downsampledCloud_1, normalsFromCloud_1, keypointsFromCloud_1, feature_radius, descriptors1);
std::cout << "first cloud: descriptor computed " << std::endl;
compute_PFH_features_at_keypoints(downsampledCloud_2, normalsFromCloud_2, keypointsFromCloud_2, feature_radius, descriptors2);
std::cout << "second cloud: descriptor computed " << std::endl;

// Find feature correspondences
std::vector<int> correspondences;
std::vector<float> correspondence_scores;
find_feature_correspondences(descriptors1, descriptors2, correspondences, correspondence_scores);

// Print out ( number of keypoints / number of points )
std::cout << "First cloud: Found " << keypointsFromCloud_1->size() << " keypoints "
    << "out of " << downsampledCloud_1->size() << " total points." << std::endl;
std::cout << "Second cloud: Found " << keypointsFromCloud_2->size() << " keypoints "
    << "out of " << downsampledCloud_2->size() << " total points." << std::endl;

// Visualize the two point clouds and their feature correspondences
visualize_correspondences(cloud_1, keypointsFromCloud_1, cloud_2_filtered, keypointsFromCloud_2, correspondences, correspondence_scores);

结果图像如下所示：

Answer 1

数据和预处理

似乎您正在尝试将仅包含对象的点云与对象在场景内的点云进行匹配。

为了获得一致且稳定的结果，请事先从场景中提取所有对象，然后尝试将参考与所有检测到的对象匹配，然后选择最佳匹配。

描述符

我使用 SHOT 描述符而不是 PFH 体验了更好的结果。

Here ，您可以从PCL的作者那里获得有关对象识别的更多信息，他们在其中描述和解释了对象识别的整个流程。