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Added util3d::segmentObstaclesFromGround() method, templated some PCL methods (new rtabmap/core/impl/util3d.hpp)

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git-svn-id: http://rtabmap.googlecode.com/svn/trunk/rtabmap@1921 f169173b-cf89-36c8-b27e-44dbe73f0c83
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matlabbe
2014-10-24 16:58:32 +00:00
parent 2bd32e5a95
commit 60b0fd2e98
12 changed files with 658 additions and 626 deletions
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corelib/include/rtabmap/core/impl/util3d.hpp Normal file
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@@ -0,0 +1,428 @@
/*
Copyright (c) 2010-2014, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the Universite de Sherbrooke nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef UTIL3D_HPP_
#define UTIL3D_HPP_
#include <rtabmap/utilite/ULogger.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/random_sample.h>
#include <pcl/filters/passthrough.h>
#include <pcl/filters/filter.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/common/transforms.h>
#include <pcl/common/common.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>
#include <pcl/segmentation/extract_clusters.h>
namespace rtabmap{
namespace util3d{
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr voxelize(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
float voxelSize)
{
typedef typename pcl::PointCloud<PointT> PointCloud;
typedef typename PointCloud::Ptr PointCloudPtr;
UASSERT(voxelSize > 0.0f);
PointCloudPtr output(new PointCloud);
pcl::VoxelGrid<PointT> filter;
filter.setLeafSize(voxelSize, voxelSize, voxelSize);
filter.setInputCloud(cloud);
filter.filter(*output);
return output;
}
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr sampling(
const typename pcl::PointCloud<PointT>::Ptr & cloud, int samples)
{
typedef typename pcl::PointCloud<PointT> PointCloud;
typedef typename PointCloud::Ptr PointCloudPtr;
UASSERT(samples > 0);
PointCloudPtr output(new PointCloud);
pcl::RandomSample<PointT> filter;
filter.setSample(samples);
filter.setInputCloud(cloud);
filter.filter(*output);
return output;
}
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr passThrough(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const std::string & axis,
float min,
float max)
{
typedef typename pcl::PointCloud<PointT> PointCloud;
typedef typename PointCloud::Ptr PointCloudPtr;
UASSERT(max > min);
UASSERT(axis.compare("x") == 0 || axis.compare("y") == 0 || axis.compare("z") == 0);
PointCloudPtr output(new PointCloud);
pcl::PassThrough<PointT> filter;
filter.setFilterFieldName(axis);
filter.setFilterLimits(min, max);
filter.setInputCloud(cloud);
filter.filter(*output);
return output;
}
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr RTABMAP_EXP removeNaNFromPointCloud(
const typename pcl::PointCloud<PointT>::Ptr & cloud)
{
typedef typename pcl::PointCloud<PointT> PointCloud;
typedef typename PointCloud::Ptr PointCloudPtr;
PointCloudPtr output(new PointCloud);
std::vector<int> indices;
pcl::removeNaNFromPointCloud<PointT>(*cloud, *output, indices);
return output;
}
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr RTABMAP_EXP removeNaNNormalsFromPointCloud(
const typename pcl::PointCloud<PointT>::Ptr & cloud)
{
typedef typename pcl::PointCloud<PointT> PointCloud;
typedef typename PointCloud::Ptr PointCloudPtr;
PointCloudPtr output(new PointCloud);
std::vector<int> indices;
pcl::removeNaNNormalsFromPointCloud<PointT>(*cloud, *output, indices);
return output;
}
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr RTABMAP_EXP transformPointCloud(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const Transform & transform)
{
typedef typename pcl::PointCloud<PointT> PointCloud;
typedef typename PointCloud::Ptr PointCloudPtr;
PointCloudPtr output(new PointCloud);
pcl::transformPointCloud<PointT>(*cloud, *output, transformToEigen4f(transform));
return output;
}
template<typename PointT>
PointT transformPoint(
const PointT & pt,
const Transform & transform)
{
return pcl::transformPoint(pt, transformToEigen3f(transform));
}
template<typename PointT>
void segmentObstaclesFromGround(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
pcl::IndicesPtr & ground,
pcl::IndicesPtr & obstacles,
float normalRadiusSearch,
float groundNormalAngle,
int minClusterSize)
{
ground.reset(new std::vector<int>);
obstacles.reset(new std::vector<int>);
// Find the ground
pcl::IndicesPtr flatSurfaces = util3d::normalFiltering<PointT>(
cloud,
groundNormalAngle,
Eigen::Vector4f(0,0,1,0),
normalRadiusSearch*2.0f,
Eigen::Vector4f(0,0,100,0));
int biggestFlatSurfaceIndex;
std::vector<pcl::IndicesPtr> clusteredFlatSurfaces = util3d::extractClusters<PointT>(
cloud,
flatSurfaces,
normalRadiusSearch*2.0f,
minClusterSize,
std::numeric_limits<int>::max(),
&biggestFlatSurfaceIndex);
// cluster all surfaces for which the centroid is in the Z-range of the bigger surface
ground = clusteredFlatSurfaces.at(biggestFlatSurfaceIndex);
Eigen::Vector4f min,max;
pcl::getMinMax3D<PointT>(*cloud, *clusteredFlatSurfaces.at(biggestFlatSurfaceIndex), min, max);
for(unsigned int i=0; i<clusteredFlatSurfaces.size(); ++i)
{
if((int)i!=biggestFlatSurfaceIndex)
{
Eigen::Vector4f centroid;
pcl::compute3DCentroid<PointT>(*cloud, *clusteredFlatSurfaces.at(i), centroid);
if(centroid[2] >= min[2] && centroid[2] <= max[2])
{
ground = util3d::concatenate(ground, clusteredFlatSurfaces.at(i));
}
}
}
if(ground->size() != cloud->size())
{
// Remove ground
pcl::IndicesPtr otherStuffIndices = util3d::extractNegativeIndices<PointT>(cloud, ground);
//Cluster remaining stuff (obstacles)
std::vector<pcl::IndicesPtr> clusteredObstaclesSurfaces = util3d::extractClusters<PointT>(
cloud,
otherStuffIndices,
normalRadiusSearch*2.0f,
minClusterSize);
// merge indices
obstacles = util3d::concatenate(clusteredObstaclesSurfaces);
}
}
template<typename PointT>
void projectCloudOnXYPlane(
typename pcl::PointCloud<PointT>::Ptr & cloud)
{
for(unsigned int i=0; i<cloud->size(); ++i)
{
cloud->at(i).z = 0;
}
}
template<typename PointT>
pcl::IndicesPtr radiusFiltering(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
float radiusSearch,
int minNeighborsInRadius)
{
pcl::IndicesPtr indices(new std::vector<int>);
return radiusFiltering<PointT>(cloud, indices, radiusSearch, minNeighborsInRadius);
}
template<typename PointT>
pcl::IndicesPtr radiusFiltering(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float radiusSearch,
int minNeighborsInRadius)
{
typedef typename pcl::search::KdTree<PointT> KdTree;
typedef typename KdTree::Ptr KdTreePtr;
KdTreePtr tree (new KdTree(false));
if(indices->size())
{
pcl::IndicesPtr output(new std::vector<int>(indices->size()));
int oi = 0; // output iterator
tree->setInputCloud(cloud, indices);
for(unsigned int i=0; i<indices->size(); ++i)
{
std::vector<int> kIndices;
std::vector<float> kDistances;
int k = tree->radiusSearch(cloud->at(indices->at(i)), radiusSearch, kIndices, kDistances);
if(k > minNeighborsInRadius)
{
output->at(oi++) = indices->at(i);
}
}
output->resize(oi);
return output;
}
else
{
pcl::IndicesPtr output(new std::vector<int>(cloud->size()));
int oi = 0; // output iterator
tree->setInputCloud(cloud);
for(unsigned int i=0; i<cloud->size(); ++i)
{
std::vector<int> kIndices;
std::vector<float> kDistances;
int k = tree->radiusSearch(cloud->at(i), radiusSearch, kIndices, kDistances);
if(k > minNeighborsInRadius)
{
output->at(oi++) = i;
}
}
output->resize(oi);
return output;
}
}
template<typename PointT>
pcl::IndicesPtr normalFiltering(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
float angleMax,
const Eigen::Vector4f & normal,
float radiusSearch,
const Eigen::Vector4f & viewpoint)
{
pcl::IndicesPtr indices(new std::vector<int>);
return normalFiltering<PointT>(cloud, indices, angleMax, normal, radiusSearch, viewpoint);
}
template<typename PointT>
pcl::IndicesPtr normalFiltering(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float angleMax,
const Eigen::Vector4f & normal,
float radiusSearch,
const Eigen::Vector4f & viewpoint)
{
typedef typename pcl::search::KdTree<PointT> KdTree;
typedef typename KdTree::Ptr KdTreePtr;
pcl::NormalEstimation<PointT, pcl::Normal> ne;
ne.setInputCloud (cloud);
if(indices->size())
{
ne.setIndices(indices);
}
KdTreePtr tree (new KdTree(false));
if(indices->size())
{
tree->setInputCloud(cloud, indices);
}
else
{
tree->setInputCloud(cloud);
}
ne.setSearchMethod (tree);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
ne.setRadiusSearch (radiusSearch);
if(viewpoint[0] != 0 || viewpoint[1] != 0 || viewpoint[2] != 0)
{
ne.setViewPoint(viewpoint[0], viewpoint[1], viewpoint[2]);
}
ne.compute (*cloud_normals);
pcl::IndicesPtr output(new std::vector<int>(cloud_normals->size()));
int oi = 0; // output iterator
Eigen::Vector3f n(normal[0], normal[1], normal[2]);
for(unsigned int i=0; i<cloud_normals->size(); ++i)
{
Eigen::Vector4f v(cloud_normals->at(i).normal_x, cloud_normals->at(i).normal_y, cloud_normals->at(i).normal_z, 0.0f);
float angle = pcl::getAngle3D(normal, v);
if(angle < angleMax)
{
output->at(oi++) = indices->size()!=0?indices->at(i):i;
}
}
output->resize(oi);
return output;
}
template<typename PointT>
std::vector<pcl::IndicesPtr> extractClusters(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
float clusterTolerance,
int minClusterSize,
int maxClusterSize,
int * biggestClusterIndex)
{
pcl::IndicesPtr indices(new std::vector<int>);
return extractClusters<PointT>(cloud, indices, clusterTolerance, minClusterSize, maxClusterSize, biggestClusterIndex);
}
template<typename PointT>
std::vector<pcl::IndicesPtr> extractClusters(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float clusterTolerance,
int minClusterSize,
int maxClusterSize,
int * biggestClusterIndex)
{
typedef typename pcl::search::KdTree<PointT> KdTree;
typedef typename KdTree::Ptr KdTreePtr;
KdTreePtr tree(new KdTree);
pcl::EuclideanClusterExtraction<PointT> ec;
ec.setClusterTolerance (clusterTolerance);
ec.setMinClusterSize (minClusterSize);
ec.setMaxClusterSize (maxClusterSize);
ec.setInputCloud (cloud);
if(indices->size())
{
ec.setIndices(indices);
tree->setInputCloud(cloud, indices);
}
else
{
tree->setInputCloud(cloud);
}
ec.setSearchMethod (tree);
std::vector<pcl::PointIndices> cluster_indices;
ec.extract (cluster_indices);
int maxIndex=-1;
unsigned int maxSize = 0;
std::vector<pcl::IndicesPtr> output(cluster_indices.size());
for(unsigned int i=0; i<cluster_indices.size(); ++i)
{
output[i] = pcl::IndicesPtr(new std::vector<int>(cluster_indices[i].indices));
if(maxSize < cluster_indices[i].indices.size())
{
maxSize = cluster_indices[i].indices.size();
maxIndex = i;
}
}
if(biggestClusterIndex)
{
*biggestClusterIndex = maxIndex;
}
return output;
}
template<typename PointT>
pcl::IndicesPtr extractNegativeIndices(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const pcl::IndicesPtr & indices)
{
pcl::IndicesPtr output(new std::vector<int>);
pcl::ExtractIndices<PointT> extract;
extract.setInputCloud (cloud);
extract.setIndices(indices);
extract.setNegative(true);
extract.filter(*output);
return output;
}
} // util3d
} // rtabmap
#endif //UTIL3D_HPP_

295
corelib/include/rtabmap/core/util3d.h
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@@ -148,55 +148,6 @@ pcl::PointXYZ RTABMAP_EXP projectDepthTo3D(
bool smoothing,
float maxZError = 0.03f);
pcl::PointCloud<pcl::PointXYZ>::Ptr RTABMAP_EXP voxelize(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
float voxelSize);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr RTABMAP_EXP voxelize(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud,
float voxelSize);
pcl::PointCloud<pcl::PointXYZ>::Ptr RTABMAP_EXP sampling(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
int samples);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr RTABMAP_EXP sampling(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud,
int samples);
pcl::PointCloud<pcl::PointXYZ>::Ptr RTABMAP_EXP passThrough(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const std::string & axis,
float min,
float max);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr RTABMAP_EXP passThrough(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud,
const std::string & axis,
float min,
float max);
pcl::PointCloud<pcl::PointXYZ>::Ptr RTABMAP_EXP removeNaNFromPointCloud(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr RTABMAP_EXP removeNaNFromPointCloud(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud);
pcl::PointCloud<pcl::PointNormal>::Ptr RTABMAP_EXP removeNaNNormalsFromPointCloud(
const pcl::PointCloud<pcl::PointNormal>::Ptr & cloud);
pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr RTABMAP_EXP removeNaNNormalsFromPointCloud(
const pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr & cloud);
pcl::PointCloud<pcl::PointXYZ>::Ptr RTABMAP_EXP transformPointCloud(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const Transform & transform);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr RTABMAP_EXP transformPointCloud(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud,
const Transform & transform);
pcl::PointXYZ RTABMAP_EXP transformPoint(
const pcl::PointXYZ & pt,
const Transform & transform);
pcl::PointXYZRGB RTABMAP_EXP transformPoint(
const pcl::PointXYZRGB & pt,
const Transform & transform);
pcl::PointCloud<pcl::PointXYZ>::Ptr RTABMAP_EXP cloudFromDepth(
const cv::Mat & imageDepth,
float cx, float cy,
@@ -546,98 +497,6 @@ void RTABMAP_EXP rayTrace(const cv::Point2i & start,
cv::Mat RTABMAP_EXP convertMap2Image8U(const cv::Mat & map8S);
void RTABMAP_EXP projectCloudOnXYPlane(
pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud);
/**
* For convenience.
*/
pcl::IndicesPtr radiusFiltering(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
float radiusSearch,
int minNeighborsInRadius);
/**
* @brief Wrapper of the pcl::RadiusOutlierRemoval class.
*
* Points in the cloud which have less than a minimum of neighbors in the
* specified radius are filtered.
* @param cloud the input cloud.
* @param indices the input indices of the cloud to check, if empty, all points in the cloud are checked.
* @param radiusSearch the radius in meter.
* @param minNeighborsInRadius the minimum of neighbors to keep the point.
* @return the indices of the points satisfying the parameters.
*/
pcl::IndicesPtr radiusFiltering(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float radiusSearch,
int minNeighborsInRadius);
/**
* For convenience.
*/
pcl::IndicesPtr RTABMAP_EXP normalFiltering(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
float angleMax,
const Eigen::Vector4f & normal,
float radiusSearch,
const Eigen::Vector4f & viewpoint);
/**
* @brief Given a normal and a maximum angle error, keep all points of the cloud
* respecting this normal.
*
* The normals are computed using the radius search parameter (pcl::NormalEstimation class is used for this), then
* for each normal, the corresponding point is filtered if the
* angle (using pcl::getAngle3D()) with the normal specified by the user is larger than the maximum
* angle specified by the user.
* @param cloud the input cloud.
* @param indices the input indices of the cloud to process, if empty, all points in the cloud are processed.
* @param angleMax the maximum angle.
* @param normal the normal to which each point's normal is compared.
* @param radiusSearch radius parameter used for normal estimation (see pcl::NormalEstimation).
* @param viewpoint from which viewpoint the normals should be estimated (see pcl::NormalEstimation).
* @return the indices of the points which respect the normal constraint.
*/
pcl::IndicesPtr RTABMAP_EXP normalFiltering(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float angleMax,
const Eigen::Vector4f & normal,
float radiusSearch,
const Eigen::Vector4f & viewpoint);
/**
* For convenience.
*/
std::vector<pcl::IndicesPtr> RTABMAP_EXP extractClusters(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
float clusterTolerance,
int minClusterSize,
int maxClusterSize = std::numeric_limits<int>::max(),
int * biggestClusterIndex = 0);
/**
* @brief Wrapper of the pcl::EuclideanClusterExtraction class.
*
* Extract all clusters from a point cloud given a maximum cluster distance tolerance.
* @param cloud the input cloud.
* @param indices the input indices of the cloud to process, if empty, all points in the cloud are processed.
* @param clusterTolerance the cluster distance tolerance (see pcl::EuclideanClusterExtraction).
* @param minClusterSize minimum size of the clusters to return (see pcl::EuclideanClusterExtraction).
* @param maxClusterSize maximum size of the clusters to return (see pcl::EuclideanClusterExtraction).
* @param biggestClusterIndex the index of the biggest cluster, if the clusters are empty, a negative index is set.
* @return the indices of each cluster found.
*/
std::vector<pcl::IndicesPtr> RTABMAP_EXP extractClusters(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float clusterTolerance,
int minClusterSize,
int maxClusterSize = std::numeric_limits<int>::max(),
int * biggestClusterIndex = 0);
/**
* @brief Concatenate a vector of indices to a single vector.
*
@@ -664,11 +523,161 @@ pcl::IndicesPtr RTABMAP_EXP concatenate(
const pcl::IndicesPtr & indicesA,
const pcl::IndicesPtr & indicesB);
pcl::IndicesPtr RTABMAP_EXP extractNegativeIndices(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
///////////////////
// Templated PCL methods
///////////////////
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr voxelize(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
float voxelSize);
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr sampling(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
int samples);
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr passThrough(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const std::string & axis,
float min,
float max);
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr removeNaNFromPointCloud(
const typename pcl::PointCloud<PointT>::Ptr & cloud);
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr removeNaNNormalsFromPointCloud(
const typename pcl::PointCloud<PointT>::Ptr & cloud);
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr transformPointCloud(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const Transform & transform);
template<typename PointT>
PointT transformPoint(
const PointT & pt,
const Transform & transform);
template<typename PointT>
void segmentObstaclesFromGround(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
pcl::IndicesPtr & ground,
pcl::IndicesPtr & obstacles,
float normalRadiusSearch,
float groundNormalAngle,
int minClusterSize);
template<typename PointT>
void projectCloudOnXYPlane(
typename pcl::PointCloud<PointT>::Ptr & cloud);
/**
* For convenience.
*/
template<typename PointT>
pcl::IndicesPtr radiusFiltering(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
float radiusSearch,
int minNeighborsInRadius);
/**
* @brief Wrapper of the pcl::RadiusOutlierRemoval class.
*
* Points in the cloud which have less than a minimum of neighbors in the
* specified radius are filtered.
* @param cloud the input cloud.
* @param indices the input indices of the cloud to check, if empty, all points in the cloud are checked.
* @param radiusSearch the radius in meter.
* @param minNeighborsInRadius the minimum of neighbors to keep the point.
* @return the indices of the points satisfying the parameters.
*/
template<typename PointT>
pcl::IndicesPtr radiusFiltering(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float radiusSearch,
int minNeighborsInRadius);
/**
* For convenience.
*/
template<typename PointT>
pcl::IndicesPtr normalFiltering(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
float angleMax,
const Eigen::Vector4f & normal,
float radiusSearch,
const Eigen::Vector4f & viewpoint);
/**
* @brief Given a normal and a maximum angle error, keep all points of the cloud
* respecting this normal.
*
* The normals are computed using the radius search parameter (pcl::NormalEstimation class is used for this), then
* for each normal, the corresponding point is filtered if the
* angle (using pcl::getAngle3D()) with the normal specified by the user is larger than the maximum
* angle specified by the user.
* @param cloud the input cloud.
* @param indices the input indices of the cloud to process, if empty, all points in the cloud are processed.
* @param angleMax the maximum angle.
* @param normal the normal to which each point's normal is compared.
* @param radiusSearch radius parameter used for normal estimation (see pcl::NormalEstimation).
* @param viewpoint from which viewpoint the normals should be estimated (see pcl::NormalEstimation).
* @return the indices of the points which respect the normal constraint.
*/
template<typename PointT>
pcl::IndicesPtr normalFiltering(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float angleMax,
const Eigen::Vector4f & normal,
float radiusSearch,
const Eigen::Vector4f & viewpoint);
/**
* For convenience.
*/
template<typename PointT>
std::vector<pcl::IndicesPtr> extractClusters(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
float clusterTolerance,
int minClusterSize,
int maxClusterSize = std::numeric_limits<int>::max(),
int * biggestClusterIndex = 0);
/**
* @brief Wrapper of the pcl::EuclideanClusterExtraction class.
*
* Extract all clusters from a point cloud given a maximum cluster distance tolerance.
* @param cloud the input cloud.
* @param indices the input indices of the cloud to process, if empty, all points in the cloud are processed.
* @param clusterTolerance the cluster distance tolerance (see pcl::EuclideanClusterExtraction).
* @param minClusterSize minimum size of the clusters to return (see pcl::EuclideanClusterExtraction).
* @param maxClusterSize maximum size of the clusters to return (see pcl::EuclideanClusterExtraction).
* @param biggestClusterIndex the index of the biggest cluster, if the clusters are empty, a negative index is set.
* @return the indices of each cluster found.
*/
template<typename PointT>
std::vector<pcl::IndicesPtr> extractClusters(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float clusterTolerance,
int minClusterSize,
int maxClusterSize = std::numeric_limits<int>::max(),
int * biggestClusterIndex = 0);
template<typename PointT>
pcl::IndicesPtr extractNegativeIndices(
const typename pcl::PointCloud<PointT>::Ptr & cloud,
const pcl::IndicesPtr & indices);
} // namespace util3d
} // namespace rtabmap
#include "rtabmap/core/impl/util3d.hpp"
#endif /* UTIL3D_H_ */

2
corelib/src/CMakeLists.txt
View File

@@ -126,6 +126,6 @@ INSTALL(TARGETS rtabmap_core
install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/../include/
DESTINATION "${INSTALL_INCLUDE_DIR}"
COMPONENT devel
FILES_MATCHING PATTERN "*.h"
FILES_MATCHING PATTERN "*.h" PATTERN "*.hpp"
PATTERN ".svn" EXCLUDE)

16
corelib/src/Memory.cpp
View File

@@ -1903,8 +1903,8 @@ Transform Memory::computeIcpTransform(const Signature & oldS, const Signature &
pcl::PointCloud<pcl::PointNormal>::Ptr newCloud = util3d::computeNormals(newCloudXYZ);
std::vector<int> indices;
newCloud = util3d::removeNaNNormalsFromPointCloud(newCloud);
oldCloud = util3d::removeNaNNormalsFromPointCloud(oldCloud);
newCloud = util3d::removeNaNNormalsFromPointCloud<pcl::PointNormal>(newCloud);
oldCloud = util3d::removeNaNNormalsFromPointCloud<pcl::PointNormal>(oldCloud);
// 3D
double fitness = 0;
@@ -1981,8 +1981,8 @@ Transform Memory::computeIcpTransform(const Signature & oldS, const Signature &
//voxelize
if(_icp2VoxelSize > 0.0f)
{
oldCloud = util3d::voxelize(oldCloud, _icp2VoxelSize);
newCloud = util3d::voxelize(newCloud, _icp2VoxelSize);
oldCloud = util3d::voxelize<pcl::PointXYZ>(oldCloud, _icp2VoxelSize);
newCloud = util3d::voxelize<pcl::PointXYZ>(newCloud, _icp2VoxelSize);
}
double fitness = 0.0f;
@@ -2000,7 +2000,7 @@ Transform Memory::computeIcpTransform(const Signature & oldS, const Signature &
//pcl::io::savePCDFile("lccold.pcd", *oldCloud);
//pcl::io::savePCDFile("lccnewguess.pcd", *newCloud);
newCloud = util3d::transformPointCloud(newCloud, icpT);
newCloud = util3d::transformPointCloud<pcl::PointXYZ>(newCloud, icpT);
//pcl::io::savePCDFile("lccnewicp.pcd", *newCloud);
// verify if there are enough correspondences
@@ -2094,7 +2094,7 @@ Transform Memory::computeScanMatchingTransform(
//voxelize
if(assembledOldClouds->size() && _icp2VoxelSize > 0.0f)
{
assembledOldClouds = util3d::voxelize(assembledOldClouds, _icp2VoxelSize);
assembledOldClouds = util3d::voxelize<pcl::PointXYZ>(assembledOldClouds, _icp2VoxelSize);
}
// get the new cloud
@@ -2106,7 +2106,7 @@ Transform Memory::computeScanMatchingTransform(
//voxelize
if(newCloud->size() && _icp2VoxelSize > 0.0f)
{
newCloud = util3d::voxelize(newCloud, _icp2VoxelSize);
newCloud = util3d::voxelize<pcl::PointXYZ>(newCloud, _icp2VoxelSize);
}
//pcl::io::savePCDFile("old.pcd", *assembledOldClouds);
@@ -2126,7 +2126,7 @@ Transform Memory::computeScanMatchingTransform(
UDEBUG("icpT=%s", icpT.prettyPrint().c_str());
newCloud = util3d::transformPointCloud(newCloud, icpT);
newCloud = util3d::transformPointCloud<pcl::PointXYZ>(newCloud, icpT);
//pcl::io::savePCDFile("newCorrected.pcd", *newCloud);
// verify if there enough correspondences

2
corelib/src/Odometry.cpp
View File

@@ -1104,7 +1104,7 @@ Transform OdometryICP::computeTransform(const SensorData & data, int * quality,
pcl::PointCloud<pcl::PointNormal>::Ptr newCloud = util3d::computeNormals(newCloudXYZ);
std::vector<int> indices;
newCloud = util3d::removeNaNNormalsFromPointCloud(newCloud);
newCloud = util3d::removeNaNNormalsFromPointCloud<pcl::PointNormal>(newCloud);
if(newCloudXYZ->size() != newCloud->size())
{
UWARN("removed nan normals...");

453
corelib/src/util3d.cpp
View File

@@ -33,9 +33,6 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <pcl/registration/transformation_estimation_2D.h>
#include <pcl/registration/correspondence_rejection_sample_consensus.h>
#include <pcl/registration/icp_nl.h>
#include <pcl/search/kdtree.h>
#include <pcl/filters/passthrough.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/io/pcd_io.h>
#include <pcl/common/distances.h>
#include <pcl/surface/gp3.h>
@@ -43,9 +40,6 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <pcl/surface/mls.h>
#include <pcl/ModelCoefficients.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/crop_box.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/filters/extract_indices.h>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp>
@@ -640,158 +634,6 @@ pcl::PointXYZ projectDepthTo3D(
return pt;
}
pcl::PointCloud<pcl::PointXYZ>::Ptr voxelize(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud, float voxelSize)
{
UASSERT(voxelSize > 0.0f);
pcl::PointCloud<pcl::PointXYZ>::Ptr output(new pcl::PointCloud<pcl::PointXYZ>);
pcl::VoxelGrid<pcl::PointXYZ> filter;
filter.setLeafSize(voxelSize, voxelSize, voxelSize);
filter.setInputCloud(cloud);
filter.filter(*output);
return output;
}
pcl::PointCloud<pcl::PointXYZRGB>::Ptr voxelize(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud, float voxelSize)
{
UASSERT(voxelSize > 0.0f);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr output(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::VoxelGrid<pcl::PointXYZRGB> filter;
filter.setLeafSize(voxelSize, voxelSize, voxelSize);
filter.setInputCloud(cloud);
filter.filter(*output);
return output;
}
pcl::PointCloud<pcl::PointXYZ>::Ptr sampling(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud, int samples)
{
UASSERT(samples > 0);
pcl::PointCloud<pcl::PointXYZ>::Ptr output(new pcl::PointCloud<pcl::PointXYZ>);
pcl::RandomSample<pcl::PointXYZ> filter;
filter.setSample(samples);
filter.setInputCloud(cloud);
filter.filter(*output);
return output;
}
pcl::PointCloud<pcl::PointXYZRGB>::Ptr sampling(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud, int samples)
{
UASSERT(samples > 0);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr output(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::RandomSample<pcl::PointXYZRGB> filter;
filter.setSample(samples);
filter.setInputCloud(cloud);
filter.filter(*output);
return output;
}
pcl::PointCloud<pcl::PointXYZ>::Ptr passThrough(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const std::string & axis,
float min,
float max)
{
UASSERT(max > min);
UASSERT(axis.compare("x") == 0 || axis.compare("y") == 0 || axis.compare("z") == 0);
pcl::PointCloud<pcl::PointXYZ>::Ptr output(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PassThrough<pcl::PointXYZ> filter;
filter.setFilterFieldName(axis);
filter.setFilterLimits(min, max);
filter.setInputCloud(cloud);
filter.filter(*output);
return output;
}
pcl::PointCloud<pcl::PointXYZRGB>::Ptr passThrough(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud,
const std::string & axis,
float min,
float max)
{
UASSERT(max > min);
UASSERT(axis.compare("x") == 0 || axis.compare("y") == 0 || axis.compare("z") == 0);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr output(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PassThrough<pcl::PointXYZRGB> filter;
filter.setFilterFieldName(axis);
filter.setFilterLimits(min, max);
filter.setInputCloud(cloud);
filter.filter(*output);
return output;
}
pcl::PointCloud<pcl::PointXYZ>::Ptr RTABMAP_EXP removeNaNFromPointCloud(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr output(new pcl::PointCloud<pcl::PointXYZ>);
std::vector<int> indices;
pcl::removeNaNFromPointCloud (*cloud, *output, indices);
return output;
}
pcl::PointCloud<pcl::PointXYZRGB>::Ptr RTABMAP_EXP removeNaNFromPointCloud(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud)
{
pcl::PointCloud<pcl::PointXYZRGB>::Ptr output(new pcl::PointCloud<pcl::PointXYZRGB>);
std::vector<int> indices;
pcl::removeNaNFromPointCloud (*cloud, *output, indices);
return output;
}
pcl::PointCloud<pcl::PointNormal>::Ptr RTABMAP_EXP removeNaNNormalsFromPointCloud(
const pcl::PointCloud<pcl::PointNormal>::Ptr & cloud)
{
pcl::PointCloud<pcl::PointNormal>::Ptr output(new pcl::PointCloud<pcl::PointNormal>);
std::vector<int> indices;
pcl::removeNaNNormalsFromPointCloud (*cloud, *output, indices);
return output;
}
pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr RTABMAP_EXP removeNaNNormalsFromPointCloud(
const pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr & cloud)
{
pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr output(new pcl::PointCloud<pcl::PointXYZRGBNormal>);
std::vector<int> indices;
pcl::removeNaNNormalsFromPointCloud(*cloud, *output, indices);
return output;
}
pcl::PointCloud<pcl::PointXYZ>::Ptr RTABMAP_EXP transformPointCloud(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const Transform & transform)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr output(new pcl::PointCloud<pcl::PointXYZ>);
pcl::transformPointCloud(*cloud, *output, transformToEigen4f(transform));
return output;
}
pcl::PointCloud<pcl::PointXYZRGB>::Ptr RTABMAP_EXP transformPointCloud(
const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud,
const Transform & transform)
{
pcl::PointCloud<pcl::PointXYZRGB>::Ptr output(new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::transformPointCloud(*cloud, *output, transformToEigen4f(transform));
return output;
}
pcl::PointXYZ RTABMAP_EXP transformPoint(
const pcl::PointXYZ & pt,
const Transform & transform)
{
return pcl::transformPoint(pt, transformToEigen3f(transform));
}
pcl::PointXYZRGB RTABMAP_EXP transformPoint(
const pcl::PointXYZRGB & pt,
const Transform & transform)
{
return pcl::transformPoint(pt, transformToEigen3f(transform));
}
pcl::PointCloud<pcl::PointXYZ>::Ptr cloudFromDepth(
const cv::Mat & imageDepth,
float cx, float cy,
@@ -2029,25 +1871,25 @@ pcl::PointCloud<pcl::PointXYZ>::Ptr getICPReadyCloud(
{
if(maxDepth>0.0)
{
cloud = passThrough(cloud, "z", 0, maxDepth);
cloud = passThrough<pcl::PointXYZ>(cloud, "z", 0, maxDepth);
}
if(cloud->size())
{
if(voxel>0)
{
cloud = voxelize(cloud, voxel);
cloud = voxelize<pcl::PointXYZ>(cloud, voxel);
}
else if(samples>0 && (int)cloud->size() > samples)
{
cloud = sampling(cloud, samples);
cloud = sampling<pcl::PointXYZ>(cloud, samples);
}
if(cloud->size())
{
if(!transform.isNull() && !transform.isIdentity())
{
cloud = transformPointCloud(cloud, transform);
cloud = transformPointCloud<pcl::PointXYZ>(cloud, transform);
}
}
}
@@ -2112,7 +1954,7 @@ pcl::PolygonMesh::Ptr createMesh(
float gp3MaximumAngle,
bool gp3NormalConsistency)
{
pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr cloudWithNormalsNoNaN = removeNaNNormalsFromPointCloud(cloudWithNormals);
pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr cloudWithNormalsNoNaN = removeNaNNormalsFromPointCloud<pcl::PointXYZRGBNormal>(cloudWithNormals);
// Create search tree*
pcl::search::KdTree<pcl::PointXYZRGBNormal>::Ptr tree2 (new pcl::search::KdTree<pcl::PointXYZRGBNormal>);
@@ -2538,80 +2380,35 @@ bool occupancy2DFromCloud3D(
pcl::PointCloud<pcl::PointXYZ>::Ptr obstaclesCloud(new pcl::PointCloud<pcl::PointXYZ>);
//voxelize
pcl::PointCloud<pcl::PointXYZRGB>::Ptr voxelizedCloud = util3d::voxelize(cloud, cellSize);
//convert to XYZ
pcl::PointCloud<pcl::PointXYZ>::Ptr cloudXYZ(new pcl::PointCloud<pcl::PointXYZ>);
pcl::copyPointCloud(*voxelizedCloud, *cloudXYZ);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr voxelizedCloud = util3d::voxelize<pcl::PointXYZRGB>(cloud, cellSize);
pcl::IndicesPtr groundIndices, obstaclesIndices;
// Find the ground
pcl::IndicesPtr flatSurfaces = util3d::normalFiltering(
cloudXYZ,
segmentObstaclesFromGround<pcl::PointXYZRGB>(cloud,
groundIndices,
obstaclesIndices,
cellSize,
groundNormalAngle,
Eigen::Vector4f(0,0,1,0),
cellSize*2.0f,
Eigen::Vector4f(0,0,100,0));
minClusterSize);
int biggestFlatSurfaceIndex;
std::vector<pcl::IndicesPtr> clusteredFlatSurfaces = util3d::extractClusters(
cloudXYZ,
flatSurfaces,
cellSize*2.0f,
minClusterSize,
std::numeric_limits<int>::max(),
&biggestFlatSurfaceIndex);
// cluster all surfaces for which the centroid is in the Z-range of the bigger surface
groundIndices = clusteredFlatSurfaces.at(biggestFlatSurfaceIndex);
Eigen::Vector4f min,max;
pcl::getMinMax3D(*cloudXYZ, *clusteredFlatSurfaces.at(biggestFlatSurfaceIndex), min, max);
for(unsigned int i=0; i<clusteredFlatSurfaces.size(); ++i)
if(groundIndices->size())
{
if((int)i!=biggestFlatSurfaceIndex)
{
Eigen::Vector4f centroid;
pcl::compute3DCentroid(*cloudXYZ, *clusteredFlatSurfaces.at(i), centroid);
if(centroid[2] >= min[2] && centroid[2] <= max[2])
{
groundIndices = util3d::concatenate(groundIndices, clusteredFlatSurfaces.at(i));
}
}
pcl::copyPointCloud(*cloud, *groundIndices, *groundCloud);
//project on XY plane
util3d::projectCloudOnXYPlane<pcl::PointXYZ>(groundCloud);
//voxelize to grid cell size
groundCloud = util3d::voxelize<pcl::PointXYZ>(groundCloud, cellSize);
}
pcl::copyPointCloud(*cloudXYZ, *groundIndices, *groundCloud);
if(groundIndices->size() != cloudXYZ->size())
if(obstaclesIndices->size())
{
// Remove ground
pcl::IndicesPtr otherStuffIndices = util3d::extractNegativeIndices(cloudXYZ, groundIndices);
//Cluster remaining stuff (obstacles)
std::vector<pcl::IndicesPtr> clusteredObstaclesSurfaces = util3d::extractClusters(
cloudXYZ,
otherStuffIndices,
cellSize*2.0f,
minClusterSize);
// merge indices
obstaclesIndices = util3d::concatenate(clusteredObstaclesSurfaces);
if(obstaclesIndices->size())
{
pcl::copyPointCloud(*cloudXYZ, *obstaclesIndices, *obstaclesCloud);
}
pcl::copyPointCloud(*cloud, *obstaclesIndices, *obstaclesCloud);
//project on XY plane
util3d::projectCloudOnXYPlane<pcl::PointXYZ>(obstaclesCloud);
//voxelize to grid cell size
obstaclesCloud = util3d::voxelize<pcl::PointXYZ>(obstaclesCloud, cellSize);
}
//project on XY plane
util3d::projectCloudOnXYPlane(groundCloud);
util3d::projectCloudOnXYPlane(obstaclesCloud);
//voxelize to grid cell size
groundCloud = util3d::voxelize(groundCloud, cellSize);
obstaclesCloud = util3d::voxelize(obstaclesCloud, cellSize);
ground = cv::Mat();
if(groundCloud->size())
{
@@ -2846,7 +2643,7 @@ cv::Mat create2DMap(const std::map<int, Transform> & poses,
{
if(uContains(scans, iter->first))
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud = util3d::transformPointCloud(scans.at(iter->first), iter->second);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud = transformPointCloud<pcl::PointXYZ>(scans.at(iter->first), iter->second);
pcl::PointXYZ min, max;
pcl::getMinMax3D(*cloud, min, max);
minMax.push_back(min);
@@ -3052,195 +2849,6 @@ cv::Mat convertMap2Image8U(const cv::Mat & map8S)
return map8U;
}
void projectCloudOnXYPlane(
pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud)
{
for(unsigned int i=0; i<cloud->size(); ++i)
{
cloud->at(i).z = 0;
}
}
pcl::IndicesPtr radiusFiltering(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
float radiusSearch,
int minNeighborsInRadius)
{
pcl::IndicesPtr indices(new std::vector<int>);
return radiusFiltering(cloud, indices, radiusSearch, minNeighborsInRadius);
}
pcl::IndicesPtr radiusFiltering(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float radiusSearch,
int minNeighborsInRadius)
{
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> (false));
if(indices->size())
{
pcl::IndicesPtr output(new std::vector<int>(indices->size()));
int oi = 0; // output iterator
tree->setInputCloud(cloud, indices);
for(unsigned int i=0; i<indices->size(); ++i)
{
std::vector<int> kIndices;
std::vector<float> kDistances;
int k = tree->radiusSearch(cloud->at(indices->at(i)), radiusSearch, kIndices, kDistances);
if(k > minNeighborsInRadius)
{
output->at(oi++) = indices->at(i);
}
}
output->resize(oi);
return output;
}
else
{
pcl::IndicesPtr output(new std::vector<int>(cloud->size()));
int oi = 0; // output iterator
tree->setInputCloud(cloud);
for(unsigned int i=0; i<cloud->size(); ++i)
{
std::vector<int> kIndices;
std::vector<float> kDistances;
int k = tree->radiusSearch(cloud->at(i), radiusSearch, kIndices, kDistances);
if(k > minNeighborsInRadius)
{
output->at(oi++) = i;
}
}
output->resize(oi);
return output;
}
}
pcl::IndicesPtr normalFiltering(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
float angleMax,
const Eigen::Vector4f & normal,
float radiusSearch,
const Eigen::Vector4f & viewpoint)
{
pcl::IndicesPtr indices(new std::vector<int>);
return normalFiltering(cloud, indices, angleMax, normal, radiusSearch, viewpoint);
}
pcl::IndicesPtr normalFiltering(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float angleMax,
const Eigen::Vector4f & normal,
float radiusSearch,
const Eigen::Vector4f & viewpoint)
{
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setInputCloud (cloud);
if(indices->size())
{
ne.setIndices(indices);
}
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
if(indices->size())
{
tree->setInputCloud(cloud, indices);
}
else
{
tree->setInputCloud(cloud);
}
ne.setSearchMethod (tree);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
ne.setRadiusSearch (radiusSearch);
if(viewpoint[0] != 0 || viewpoint[1] != 0 || viewpoint[2] != 0)
{
ne.setViewPoint(viewpoint[0], viewpoint[1], viewpoint[2]);
}
ne.compute (*cloud_normals);
pcl::IndicesPtr output(new std::vector<int>(cloud_normals->size()));
int oi = 0; // output iterator
Eigen::Vector3f n(normal[0], normal[1], normal[2]);
for(unsigned int i=0; i<cloud_normals->size(); ++i)
{
Eigen::Vector4f v(cloud_normals->at(i).normal_x, cloud_normals->at(i).normal_y, cloud_normals->at(i).normal_z, 0.0f);
float angle = pcl::getAngle3D(normal, v);
if(angle < angleMax)
{
output->at(oi++) = indices->size()!=0?indices->at(i):i;
}
}
output->resize(oi);
return output;
}
std::vector<pcl::IndicesPtr> extractClusters(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
float clusterTolerance,
int minClusterSize,
int maxClusterSize,
int * biggestClusterIndex)
{
pcl::IndicesPtr indices(new std::vector<int>);
return extractClusters(cloud, indices, clusterTolerance, minClusterSize, maxClusterSize, biggestClusterIndex);
}
std::vector<pcl::IndicesPtr> extractClusters(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const pcl::IndicesPtr & indices,
float clusterTolerance,
int minClusterSize,
int maxClusterSize,
int * biggestClusterIndex)
{
pcl::search::KdTree<pcl::PointXYZ>::Ptr kdTree(new pcl::search::KdTree<pcl::PointXYZ>);
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (clusterTolerance);
ec.setMinClusterSize (minClusterSize);
ec.setMaxClusterSize (maxClusterSize);
ec.setInputCloud (cloud);
if(indices->size())
{
ec.setIndices(indices);
kdTree->setInputCloud(cloud, indices);
}
else
{
kdTree->setInputCloud(cloud);
}
ec.setSearchMethod (kdTree);
std::vector<pcl::PointIndices> cluster_indices;
ec.extract (cluster_indices);
int maxIndex=-1;
unsigned int maxSize = 0;
std::vector<pcl::IndicesPtr> output(cluster_indices.size());
for(unsigned int i=0; i<cluster_indices.size(); ++i)
{
output[i] = pcl::IndicesPtr(new std::vector<int>(cluster_indices[i].indices));
if(maxSize < cluster_indices[i].indices.size())
{
maxSize = cluster_indices[i].indices.size();
maxIndex = i;
}
}
if(biggestClusterIndex)
{
*biggestClusterIndex = maxIndex;
}
return output;
}
pcl::IndicesPtr concatenate(const std::vector<pcl::IndicesPtr> & indices)
{
//compute total size
@@ -3273,19 +2881,6 @@ pcl::IndicesPtr concatenate(const pcl::IndicesPtr & indicesA, const pcl::Indices
return ind;
}
pcl::IndicesPtr extractNegativeIndices(
const pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud,
const pcl::IndicesPtr & indices)
{
pcl::IndicesPtr output(new std::vector<int>);
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud (cloud);
extract.setIndices(indices);
extract.setNegative(true);
extract.filter(*output);
return output;
}
}
}