这次我们将使用Iterative Closest Point algorithm来递增的注册一系列的点云。

这个主意来自于把所有的点云转换成第一个点云的框架，通过找到每个连续点云间最好的装换，并且计算整个点云的转换。

你的数据集应该由重新排列的，在一个相同的框架里面重叠的点云构成。

/*
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/* \author Radu Bogdan Rusu
 * adaptation Raphael Favier*/
#include <boost/make_shared.hpp>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/point_representation.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/filter.h>
#include <pcl/features/normal_3d.h>
#include <pcl/registration/icp.h>
#include <pcl/registration/icp_nl.h>
#include <pcl/registration/transforms.h>
#include <pcl/visualization/pcl_visualizer.h>
using pcl::visualization::PointCloudColorHandlerGenericField;
using pcl::visualization::PointCloudColorHandlerCustom;
//convenient typedefs
typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloud;
typedef pcl::PointNormal PointNormalT;
typedef pcl::PointCloud<PointNormalT> PointCloudWithNormals;
// This is a tutorial so we can afford having global variables 
//our visualizer
pcl::visualization::PCLVisualizer *p;
//its left and right viewports
int vp_1, vp_2;
//convenient structure to handle our pointclouds
struct PCD
{
PointCloud::Ptr cloud;
std::string f_name;
PCD() : cloud (new PointCloud) {};
};
struct PCDComparator
{
bool operator () (const PCD& p1, const PCD& p2)
{
return (p1.f_name < p2.f_name);
}
};
// Define a new point representation for < x, y, z, curvature >
class MyPointRepresentation : public pcl::PointRepresentation <PointNormalT>
{
using pcl::PointRepresentation<PointNormalT>::nr_dimensions_;
public:
MyPointRepresentation ()
{
// Define the number of dimensions
nr_dimensions_ = 4;
}
// Override the copyToFloatArray method to define our feature vector
virtual void copyToFloatArray (const PointNormalT &p, float * out) const
{
// < x, y, z, curvature >
out[0] = p.x;
out[1] = p.y;
out[2] = p.z;
out[3] = p.curvature;
}
};

/** \brief Display source and target on the first viewport of the visualizer
 *
 */
void showCloudsLeft(const PointCloud::Ptr cloud_target, const PointCloud::Ptr cloud_source)
{
p->removePointCloud ("vp1_target");
p->removePointCloud ("vp1_source");
PointCloudColorHandlerCustom<PointT> tgt_h (cloud_target, 0, 255, 0);
PointCloudColorHandlerCustom<PointT> src_h (cloud_source, 255, 0, 0);
p->addPointCloud (cloud_target, tgt_h, "vp1_target", vp_1);
p->addPointCloud (cloud_source, src_h, "vp1_source", vp_1);
PCL_INFO ("Press q to begin the registration.\n");
p-> spin();
}

/** \brief Display source and target on the second viewport of the visualizer
 *
 */
void showCloudsRight(const PointCloudWithNormals::Ptr cloud_target, const PointCloudWithNormals::Ptr cloud_source)
{
p->removePointCloud ("source");
p->removePointCloud ("target");
PointCloudColorHandlerGenericField<PointNormalT> tgt_color_handler (cloud_target, "curvature");
if (!tgt_color_handler.isCapable ())
PCL_WARN ("Cannot create curvature color handler!");
PointCloudColorHandlerGenericField<PointNormalT> src_color_handler (cloud_source, "curvature");
if (!src_color_handler.isCapable ())
PCL_WARN ("Cannot create curvature color handler!");
p->addPointCloud (cloud_target, tgt_color_handler, "target", vp_2);
p->addPointCloud (cloud_source, src_color_handler, "source", vp_2);
p->spinOnce();
}

/** \brief Load a set of PCD files that we want to register together
  * \param argc the number of arguments (pass from main ())
  * \param argv the actual command line arguments (pass from main ())
  * \param models the resultant vector of point cloud datasets
  */
void loadData (int argc, char **argv, std::vector<PCD, Eigen::aligned_allocator<PCD> > &models)
{
std::string extension (".pcd");
// Suppose the first argument is the actual test model
for (int i = 1; i < argc; i++)
{
std::string fname = std::string (argv[i]);
// Needs to be at least 5: .plot
if (fname.size () <= extension.size ())
continue;
std::transform (fname.begin (), fname.end (), fname.begin (), (int(*)(int))tolower);
//check that the argument is a pcd file
if (fname.compare (fname.size () - extension.size (), extension.size (), extension) == 0)
{
// Load the cloud and saves it into the global list of models
PCD m;
m.f_name = argv[i];
pcl::io::loadPCDFile (argv[i], *m.cloud);
//remove NAN points from the cloud
std::vector<int> indices;
pcl::removeNaNFromPointCloud(*m.cloud,*m.cloud, indices);
models.push_back (m);
}
}
}

/** \brief Align a pair of PointCloud datasets and return the result
  * \param cloud_src the source PointCloud
  * \param cloud_tgt the target PointCloud
  * \param output the resultant aligned source PointCloud
  * \param final_transform the resultant transform between source and target
  */
void pairAlign (const PointCloud::Ptr cloud_src, const PointCloud::Ptr cloud_tgt, PointCloud::Ptr output, Eigen::Matrix4f &final_transform, bool downsample = false)
{
//
// Downsample for consistency and speed
// \note enable this for large datasets
PointCloud::Ptr src (new PointCloud);
PointCloud::Ptr tgt (new PointCloud);
pcl::VoxelGrid<PointT> grid;
if (downsample)
{
grid.setLeafSize (0.05, 0.05, 0.05);
grid.setInputCloud (cloud_src);
grid.filter (*src);
grid.setInputCloud (cloud_tgt);
grid.filter (*tgt);
}
else
{
src = cloud_src;
tgt = cloud_tgt;
}
// Compute surface normals and curvature
PointCloudWithNormals::Ptr points_with_normals_src (new PointCloudWithNormals);
PointCloudWithNormals::Ptr points_with_normals_tgt (new PointCloudWithNormals);
pcl::NormalEstimation<PointT, PointNormalT> norm_est;
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
norm_est.setSearchMethod (tree);
norm_est.setKSearch (30);
norm_est.setInputCloud (src);
norm_est.compute (*points_with_normals_src);
pcl::copyPointCloud (*src, *points_with_normals_src);
norm_est.setInputCloud (tgt);
norm_est.compute (*points_with_normals_tgt);
pcl::copyPointCloud (*tgt, *points_with_normals_tgt);
//
// Instantiate our custom point representation (defined above) ...
MyPointRepresentation point_representation;
// ... and weight the 'curvature' dimension so that it is balanced against x, y, and z
float alpha[4] = {1.0, 1.0, 1.0, 1.0};
point_representation.setRescaleValues (alpha);
//
// Align
pcl::IterativeClosestPointNonLinear<PointNormalT, PointNormalT> reg;
reg.setTransformationEpsilon (1e-6);
// Set the maximum distance between two correspondences (src<->tgt) to 10cm
// Note: adjust this based on the size of your datasets
reg.setMaxCorrespondenceDistance (0.1);  
// Set the point representation
reg.setPointRepresentation (boost::make_shared<const MyPointRepresentation> (point_representation));
reg.setInputSource (points_with_normals_src);
reg.setInputTarget (points_with_normals_tgt);
//
// Run the same optimization in a loop and visualize the results
Eigen::Matrix4f Ti = Eigen::Matrix4f::Identity (), prev, targetToSource;
PointCloudWithNormals::Ptr reg_result = points_with_normals_src;
reg.setMaximumIterations (2);
for (int i = 0; i < 30; ++i)
{
PCL_INFO ("Iteration Nr. %d.\n", i);
// save cloud for visualization purpose
points_with_normals_src = reg_result;
// Estimate
reg.setInputSource (points_with_normals_src);
reg.align (*reg_result);
//accumulate transformation between each Iteration
Ti = reg.getFinalTransformation () * Ti;
//if the difference between this transformation and the previous one
//is smaller than the threshold, refine the process by reducing
//the maximal correspondence distance
if (fabs ((reg.getLastIncrementalTransformation () - prev).sum ()) < reg.getTransformationEpsilon ())
reg.setMaxCorrespondenceDistance (reg.getMaxCorrespondenceDistance () - 0.001);
prev = reg.getLastIncrementalTransformation ();
// visualize current state
showCloudsRight(points_with_normals_tgt, points_with_normals_src);
}
//
// Get the transformation from target to source
targetToSource = Ti.inverse();
//
// Transform target back in source frame
pcl::transformPointCloud (*cloud_tgt, *output, targetToSource);
p->removePointCloud ("source");
p->removePointCloud ("target");
PointCloudColorHandlerCustom<PointT> cloud_tgt_h (output, 0, 255, 0);
PointCloudColorHandlerCustom<PointT> cloud_src_h (cloud_src, 255, 0, 0);
p->addPointCloud (output, cloud_tgt_h, "target", vp_2);
p->addPointCloud (cloud_src, cloud_src_h, "source", vp_2);
PCL_INFO ("Press q to continue the registration.\n");
p->spin ();
p->removePointCloud ("source"); 
p->removePointCloud ("target");
//add the source to the transformed target
*output += *cloud_src;
final_transform = targetToSource;
}
/* ---[ */
int main (int argc, char** argv)
{
// Load data
std::vector<PCD, Eigen::aligned_allocator<PCD> > data;
loadData (argc, argv, data);
// Check user input
if (data.empty ())
{
PCL_ERROR ("Syntax is: %s <source.pcd> <target.pcd> [*]", argv[0]);
PCL_ERROR ("[*] - multiple files can be added. The registration results of (i, i+1) will be registered against (i+2), etc");
return (-1);
}
PCL_INFO ("Loaded %d datasets.", (int)data.size ());
// Create a PCLVisualizer object
p = new pcl::visualization::PCLVisualizer (argc, argv, "Pairwise Incremental Registration example");
p->createViewPort (0.0, 0, 0.5, 1.0, vp_1);
p->createViewPort (0.5, 0, 1.0, 1.0, vp_2);
PointCloud::Ptr result (new PointCloud), source, target;
Eigen::Matrix4f GlobalTransform = Eigen::Matrix4f::Identity (), pairTransform;
for (size_t i = 1; i < data.size (); ++i)
{
source = data[i-1].cloud;
target = data[i].cloud;
// Add visualization data
showCloudsLeft(source, target);
PointCloud::Ptr temp (new PointCloud);
PCL_INFO ("Aligning %s (%d) with %s (%d).\n", data[i-1].f_name.c_str (), source->points.size (), data[i].f_name.c_str (), target->points.size ());
pairAlign (source, target, temp, pairTransform, true);
//transform current pair into the global transform
pcl::transformPointCloud (*temp, *result, GlobalTransform);
//update the global transform
GlobalTransform = GlobalTransform * pairTransform;
//save aligned pair, transformed into the first cloud's frame
std::stringstream ss;
ss << i << ".pcd";
pcl::io::savePCDFile (ss.str (), *result, true);
}
}
/* ]--- */

我们这次直接看看核心函数的功能

主函数检查用户输入，加载数据通过矢量的形式并且开启了一个匹配注册的过程，在找到一对点云的转换后，这一对点云将转换到第一个点云的框架。全局的转换就会被更新。

int main (int argc, char** argv)
{
// Load data
std::vector<PCD, Eigen::aligned_allocator<PCD> > data;
loadData (argc, argv, data);
// Check user input
if (data.empty ())
{
PCL_ERROR ("Syntax is: %s <source.pcd> <target.pcd> [*]", argv[0]);
PCL_ERROR ("[*] - multiple files can be added. The registration results of (i, i+1) will be registered against (i+2), etc");
return (-1);
}
PCL_INFO ("Loaded %d datasets.", (int)data.size ());
// Create a PCLVisualizer object
p = new pcl::visualization::PCLVisualizer (argc, argv, "Pairwise Incremental Registration example");
p->createViewPort (0.0, 0, 0.5, 1.0, vp_1);
p->createViewPort (0.5, 0, 1.0, 1.0, vp_2);
PointCloud::Ptr result (new PointCloud), source, target;
Eigen::Matrix4f GlobalTransform = Eigen::Matrix4f::Identity (), pairTransform;
for (size_t i = 1; i < data.size (); ++i)
{
source = data[i-1].cloud;
target = data[i].cloud;
// Add visualization data
showCloudsLeft(source, target);
PointCloud::Ptr temp (new PointCloud);
PCL_INFO ("Aligning %s (%d) with %s (%d).\n", data[i-1].f_name.c_str (), source->points.size (), data[i].f_name.c_str (), target->points.size ());
pairAlign (source, target, temp, pairTransform, true);
//transform current pair into the global transform
pcl::transformPointCloud (*temp, *result, GlobalTransform);
//update the global transform
GlobalTransform = GlobalTransform * pairTransform;
//save aligned pair, transformed into the first cloud's frame
std::stringstream ss;
ss << i << ".pcd";
pcl::io::savePCDFile (ss.str (), *result, true);
}
}
/* ]--- */

加载数据是很简单的。我们迭代了每个程序的参数。

对于每一个参数，我们检测是否它链接到一个pcd文件。如果是，我们就创造一个PCD对象来添加点云向量。

void loadData (int argc, char **argv, std::vector<PCD, Eigen::aligned_allocator<PCD> > &models)
{
std::string extension (".pcd");
// Suppose the first argument is the actual test model
for (int i = 1; i < argc; i++)
{
std::string fname = std::string (argv[i]);
// Needs to be at least 5: .plot
if (fname.size () <= extension.size ())
continue;
std::transform (fname.begin (), fname.end (), fname.begin (), (int(*)(int))tolower);
//check that the argument is a pcd file
if (fname.compare (fname.size () - extension.size (), extension.size (), extension) == 0)
{
// Load the cloud and saves it into the global list of models
PCD m;
m.f_name = argv[i];
pcl::io::loadPCDFile (argv[i], *m.cloud);
//remove NAN points from the cloud
std::vector<int> indices;
pcl::removeNaNFromPointCloud(*m.cloud,*m.cloud, indices);
models.push_back (m);
}
}
}

我们现在实际已经到了点的注册

void pairAlign (const PointCloud::Ptr cloud_src, const PointCloud::Ptr cloud_tgt, PointCloud::Ptr output, Eigen::Matrix4f &final_transform, bool downsample = false)

接下去我们进行对点云的降低采样，然后创建ICP这个对象。

一旦最好的转换找到了，我们将把它进行反转并把它放到目标点云里面。

//
// Get the transformation from target to source
targetToSource = Ti.inverse();
//
// Transform target back in source frame
pcl::transformPointCloud (*cloud_tgt, *output, targetToSource);
[...]
*output += *cloud_tgt;
final_transform = targetToSource;

 

运行

/pairwise_incremental_registration capture000[1-5].pcd

 

结果

运行最终结果

pcl_viewer 1.pcd 2.pcd 3.pcd 4.pcd