PCL ::Features: Hands-on - Point Cloud Library · Hands-on In this part of the session, we’ll...

PCL :: Features: Hands-onSuat Gedikli

November 6, 2011

Hands-on

In this part of the session, we’ll have a look to an exampleapplication that uses most of the methods described so far.

I Reading PCD files and displaying point cloudsI Plane detection and segmenationI Euclidean clusteringI Keypoint detection using various methodsI Feature extraction using various methodsI Feature matchingI Feature rejectionI Initial Alignment (manual)I Registration using ICPI Surface Reconstruction (Triangulation)

Point Cloud Library (PCL)

Accessing the Code

Code is be available online here:http://pointclouds.org/media/iccv2011.htmlchange to the folder where you extracted the source code andrun:> make release

the binaries should be in the folder ./build/Release We willconcentrate on the sourcode tutorial.cpp. Other useful tutorialcode is also available in the src folder and also will be installedinto the ./build/Release folder.


Running the Example

To run the application just type in> ./tutorial <source-pcd-file> <target-pcd-file> <

keypoint-detection-method> <feature-descriptor> <surface-

reconstruction-method>

the available keypoint detectors are listed if you run theexample without arguments> ./tutorial

To use the SHOT descriptor with the SIFT keypoint detectorand Marching Cubes as the desired surface reconstructionmethod, type:> ./tutorial 1 2 2


The Example Framework

tutorial.cpp:

The example framework is implemented in the template-class:template<typename FeatureType> class ICCVTutorial

which is templated on the feature-descriptor type used forcorrespondence finding.



tutorial.cpp:

The constructorICCVTutorial (pcl::Keypoint<pcl::PointXYZRGB, pcl::PointXYZI

>::Ptr, typename pcl::Feature<pcl::PointXYZRGB, FeatureType

>::Ptr, typename pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr

, typename pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr);



tutorial.cpp:

The actual work is done in following methods:void segmentation (input, segmented)const;

void detectKeypoints (input, keypoints)const;

void extractDescriptors (input, keypoints, features);void findCorrespondences (source, target, correspondences)

const;void filterCorrespondences ();

void determineInitialTransformation ();

void determineFinalTransformation ();

void reconstructSurface ();


Keypoint Detectors

tutorial.cpp:

Using different keypoint detectors: main ()Sift:pcl::SIFTKeypoint<...>* sift3D = new pcl::SIFTKeypoint<...>;

sift3D->setScales(0.01, 3, 2);

sift3D->setMinimumContrast(0.0);


Keypoint Detectors

tutorial.cpp:

Using different keypoint detectors: main ()Harris-like:pcl::HarrisKeypoint3D<...>* harris3D = new pcl::

HarrisKeypoint3D<...>;harris3D->setMethod(...);


Feature Descriptors

tutorial.cpp:

Using different feature descriptors: main ()Since CorrespondenceViewer is templated on featuredescriptor type:


Feature Descriptors

tutorial.cpp:

Using different feature descriptors: main ()Since CorrespondenceViewer is templated on featuredescriptor type:CorrespondenceViewer<pcl::FPFHSignature33> ... ;

CorrespondenceViewer<pcl::SHOT> ... ;

CorrespondenceViewer<pcl::PFHSignature125> ... ;

CorrespondenceViewer<pcl::PFHRGBSignature250> ... ;


Plane Detection

tutorial.cpp: segmentation ()

pcl::ModelCoefficients::Ptr coefficients (new pcl::

ModelCoefficients);pcl::PointIndices::Ptr inliers (new pcl::PointIndices);

pcl::SACSegmentation<pcl::PointXYZRGB> seg;

seg.setOptimizeCoefficients (true);

seg.setModelType (pcl::SACMODEL_PLANE);

seg.setMethodType (pcl::SAC_RANSAC);

seg.setDistanceThreshold (0.02);

seg.setInputCloud (source);

seg.segment (*inliers, *coefficients);


Segmentation


pcl::ExtractIndices<pcl::PointXYZRGB> extract;

extract.setInputCloud (source);

extract.setIndices (inliers);

extract.setNegative (true);

extract.filter (*segmented);


Segmentation


pcl::ExtractIndices<pcl::PointXYZRGB> extract;

extract.setInputCloud (source);

extract.setIndices (inliers);

extract.setNegative (true);

extract.filter (*segmented);


Segmentation Result


Segmentation Result


Clustering


std::vector<pcl::PointIndices> cluster_indices;

pcl::EuclideanClusterExtraction<pcl::PointXYZRGB> clustering

clustering.setClusterTolerance (0.02);

clustering.setMinClusterSize (1000);

clustering.setMaxClusterSize (250000);

clustering.setSearchMethod (tree);

clustering.setInputCloud(segmented);

clustering.extract (cluster_indices);

...


Clustering Result


Clustering Result


Keypoint Detection

tutorial.cpp: detectKeypoints ()

keypoint_detector_->setInputCloud(input);

keypoint_detector_->compute(*keypoints);


Keypoint Result


Keypoint Result


Keypoint Result


Feature Extraction

tutorial.cpp: extractDescriptors ()

feature_extractor_->setSearchSurface(input);

feature_extractor_->setInputCloud(kpts);

feature_extractor_->compute (*features);

calculate and set normals is feature_extractor is derived fromFeatureFromNormalsfeature_from_normals->setInputNormals(normals);


Feature Matching

tutorial.cpp: findCorrespondences ()

pcl::KdTreeFLANN<FeatureType> matcher;

matcher.setInputCloud (target);

std::vector<int> indices (1);

std::vector<float> distances (1);

for (size_t i = 0; i < source->size (); ++i){

matcher.nearestKSearch (*source, i, 1, indices, distances);

correspondences[i] = indices[0];

}


Feature Matching Result

source to target correspondences:


Feature Matching Result

target to source correspondences:


Correspondence Filtering

tutorial.cpp: filterCorrespondences ()

for (cIdx = 0; cIdx < source2target.size (); ++cIdx){

if (target2source[source2target[cIdx]] == cIdx)

correspondences.push_back( <cIdx, source2target[cIdx]>);

}

CorrespondenceRejectorSampleConsensus rejector

rejector.setInputCloud(source_keypoints_);

rejector.setTargetCloud(target_keypoints_);

rejector.setInputCorrespondences(correspondences_);

rejector.getCorrespondences(*correspondences_);


Correspondence Filtering Result






Initial Alignment

tutorial.cpp: determineInitialTransformation ()

TransformationEstimation<...>::Ptr te (new

TransformationEstimationSVD<...>);te->estimateRigidTransformation (source, target, corrs,

trans);transformPointCloud(source, transformed_, trans);


Registration via ICP

tutorial.cpp: determineFinalTransformation ()

Registration<...>::Ptr registration (new

IterativeClosestPoint<...>);registration->setInputCloud(source);

registration->setInputTarget (target);

registration->setMaxCorrespondenceDistance(0.05);

registration->setRANSACOutlierRejectionThreshold (0.05);

registration->setTransformationEpsilon (0.000001);

registration->setMaximumIterations (1000);

registration->align(*registered);

transformation = registration->getFinalTransformation();


Surface Reconstruction

tutorial.cpp: reconstructSurface ()

merging registered point clouds to one cloudPointCloud<...>::Ptr merged (new PointCloud<...>);

*merged = *source_transformed_;

*merged += *target_segmented_;




subsampling point cloud, so we have a equal densityVoxelGrid<PointXYZRGB> voxel_grid;

voxel_grid.setInputCloud(merged);

set voxel size to 2 mmvoxel_grid.setLeafSize(0.002, 0.002, 0.002);

voxel_grid.setDownsampleAllData(true);

voxel_grid.filter(*merged);




assemble pointcloud with normalsPointCloud<PointXYZRGBNormal>::Ptr vertices (new PointCloud

<...>);pcl::copyPointCloud(*merged, *vertices);

estimate normals using 1 cm radiusNormalEstimation<PointXYZRGB, PointXYZRGBNormal> ne;

ne.setRadiusSearch (0.01);

ne.setInputCloud (merged);

ne.compute (*vertices);




initialize search method for surface reconstructionKdTree<PointXYZRGBNormal>::Ptr tree (new KdTree<...>);

tree->setInputCloud (vertices);




reconstruct surfacesr->setSearchMethod(tree);

sr->setInputCloud(vertices);

sr->reconstruct(surface_);




result for the GreedyProjectionTriangulation method


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PCL ::Features: Hands-on - Point Cloud Library · Hands-on In this part of the session, we’ll...

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