TEST (PCL, IterativeClosestPoint_PointToPlane)
{
typedef PointNormal PointT;
PointCloud<PointT>::Ptr src (new PointCloud<PointT>);
copyPointCloud (cloud_source, *src);
PointCloud<PointT>::Ptr tgt (new PointCloud<PointT>);
copyPointCloud (cloud_target, *tgt);
PointCloud<PointT> output;
NormalEstimation<PointNormal, PointNormal> norm_est;
norm_est.setSearchMethod (search::KdTree<PointNormal>::Ptr (new search::KdTree<PointNormal>));
norm_est.setKSearch (10);
norm_est.setInputCloud (tgt);
norm_est.compute (*tgt);
IterativeClosestPoint<PointT, PointT> reg;
typedef registration::TransformationEstimationPointToPlane<PointT, PointT> PointToPlane;
boost::shared_ptr<PointToPlane> point_to_plane (new PointToPlane);
reg.setTransformationEstimation (point_to_plane);
reg.setInputCloud (src);
reg.setInputTarget (tgt);
reg.setMaximumIterations (50);
reg.setTransformationEpsilon (1e-8);
// Register
reg.align (output);
EXPECT_EQ (int (output.points.size ()), int (cloud_source.points.size ()));
// We get different results on 32 vs 64-bit systems. To address this, we've removed the explicit output test
// on the transformation matrix. Instead, we're testing to make sure the algorithm converges to a sufficiently
// low error by checking the fitness score.
/*
Eigen::Matrix4f transformation = reg.getFinalTransformation ();
EXPECT_NEAR (transformation (0, 0), 0.9046, 1e-2);
EXPECT_NEAR (transformation (0, 1), 0.0609, 1e-2);
EXPECT_NEAR (transformation (0, 2), -0.4219, 1e-2);
EXPECT_NEAR (transformation (0, 3), 0.0327, 1e-2);
EXPECT_NEAR (transformation (1, 0), -0.0328, 1e-2);
EXPECT_NEAR (transformation (1, 1), 0.9968, 1e-2);
EXPECT_NEAR (transformation (1, 2), 0.0851, 1e-2);
EXPECT_NEAR (transformation (1, 3), -0.0003, 1e-2);
EXPECT_NEAR (transformation (2, 0), 0.4250, 1e-2);
EXPECT_NEAR (transformation (2, 1), -0.0641, 1e-2);
EXPECT_NEAR (transformation (2, 2), 0.9037, 1e-2);
EXPECT_NEAR (transformation (2, 3), 0.0413, 1e-2);
EXPECT_EQ (transformation (3, 0), 0);
EXPECT_EQ (transformation (3, 1), 0);
EXPECT_EQ (transformation (3, 2), 0);
EXPECT_EQ (transformation (3, 3), 1);
*/
EXPECT_LT (reg.getFitnessScore (), 0.001);
}
void
compute (const pcl::PCLPointCloud2::ConstPtr &source,
const pcl::PCLPointCloud2::ConstPtr &target,
pcl::PCLPointCloud2 &transformed_source)
{
// Convert data to PointCloud<T>
PointCloud<PointNormal>::Ptr src (new PointCloud<PointNormal>);
PointCloud<PointNormal>::Ptr tgt (new PointCloud<PointNormal>);
fromPCLPointCloud2 (*source, *src);
fromPCLPointCloud2 (*target, *tgt);
// Estimate
TicToc tt;
tt.tic ();
print_highlight (stderr, "Computing ");
#define Scalar double
//#define Scalar float
TransformationEstimationLM<PointNormal, PointNormal, Scalar>::Ptr te (new TransformationEstimationLM<PointNormal, PointNormal, Scalar>);
//TransformationEstimationSVD<PointNormal, PointNormal, Scalar>::Ptr te (new TransformationEstimationSVD<PointNormal, PointNormal, Scalar>);
CorrespondenceEstimation<PointNormal, PointNormal, double>::Ptr cens (new CorrespondenceEstimation<PointNormal, PointNormal, double>);
//CorrespondenceEstimationNormalShooting<PointNormal, PointNormal, PointNormal>::Ptr cens (new CorrespondenceEstimationNormalShooting<PointNormal, PointNormal, PointNormal>);
//CorrespondenceEstimationNormalShooting<PointNormal, PointNormal, PointNormal, double>::Ptr cens (new CorrespondenceEstimationNormalShooting<PointNormal, PointNormal, PointNormal, double>);
cens->setInputSource (src);
cens->setInputTarget (tgt);
//cens->setSourceNormals (src);
CorrespondenceRejectorOneToOne::Ptr cor_rej_o2o (new CorrespondenceRejectorOneToOne);
CorrespondenceRejectorMedianDistance::Ptr cor_rej_med (new CorrespondenceRejectorMedianDistance);
cor_rej_med->setInputSource<PointNormal> (src);
cor_rej_med->setInputTarget<PointNormal> (tgt);
CorrespondenceRejectorSampleConsensus<PointNormal>::Ptr cor_rej_sac (new CorrespondenceRejectorSampleConsensus<PointNormal>);
cor_rej_sac->setInputSource (src);
cor_rej_sac->setInputTarget (tgt);
cor_rej_sac->setInlierThreshold (0.005);
cor_rej_sac->setMaximumIterations (10000);
CorrespondenceRejectorVarTrimmed::Ptr cor_rej_var (new CorrespondenceRejectorVarTrimmed);
cor_rej_var->setInputSource<PointNormal> (src);
cor_rej_var->setInputTarget<PointNormal> (tgt);
CorrespondenceRejectorTrimmed::Ptr cor_rej_tri (new CorrespondenceRejectorTrimmed);
IterativeClosestPoint<PointNormal, PointNormal, Scalar> icp;
icp.setCorrespondenceEstimation (cens);
icp.setTransformationEstimation (te);
icp.addCorrespondenceRejector (cor_rej_o2o);
//icp.addCorrespondenceRejector (cor_rej_var);
//icp.addCorrespondenceRejector (cor_rej_med);
//icp.addCorrespondenceRejector (cor_rej_tri);
//icp.addCorrespondenceRejector (cor_rej_sac);
icp.setInputSource (src);
icp.setInputTarget (tgt);
icp.setMaximumIterations (1000);
icp.setTransformationEpsilon (1e-10);
PointCloud<PointNormal> output;
icp.align (output);
print_info ("[done, ");
print_value ("%g", tt.toc ());
print_info (" ms : ");
print_value ("%d", output.width * output.height);
print_info (" points], has converged: ");
print_value ("%d", icp.hasConverged ());
print_info (" with score: %f\n", icp.getFitnessScore ());
Eigen::Matrix4d transformation = icp.getFinalTransformation ();
//Eigen::Matrix4f transformation = icp.getFinalTransformation ();
PCL_DEBUG ("Transformation is:\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n",
transformation (0, 0), transformation (0, 1), transformation (0, 2), transformation (0, 3),
transformation (1, 0), transformation (1, 1), transformation (1, 2), transformation (1, 3),
transformation (2, 0), transformation (2, 1), transformation (2, 2), transformation (2, 3),
transformation (3, 0), transformation (3, 1), transformation (3, 2), transformation (3, 3));
// Convert data back
pcl::PCLPointCloud2 output_source;
toPCLPointCloud2 (output, output_source);
concatenateFields (*source, output_source, transformed_source);
}
TEST (PCL, IterativeClosestPoint_PointToPlane)
{
typedef PointNormal PointT;
PointCloud<PointT>::Ptr src (new PointCloud<PointT>);
copyPointCloud (cloud_source, *src);
PointCloud<PointT>::Ptr tgt (new PointCloud<PointT>);
copyPointCloud (cloud_target, *tgt);
PointCloud<PointT> output;
NormalEstimation<PointNormal, PointNormal> norm_est;
norm_est.setSearchMethod (search::KdTree<PointNormal>::Ptr (new search::KdTree<PointNormal>));
norm_est.setKSearch (10);
norm_est.setInputCloud (tgt);
norm_est.compute (*tgt);
IterativeClosestPoint<PointT, PointT> reg;
typedef registration::TransformationEstimationPointToPlane<PointT, PointT> PointToPlane;
boost::shared_ptr<PointToPlane> point_to_plane (new PointToPlane);
reg.setTransformationEstimation (point_to_plane);
reg.setInputSource (src);
reg.setInputTarget (tgt);
reg.setMaximumIterations (50);
reg.setTransformationEpsilon (1e-8);
// Use a correspondence estimator which needs normals
registration::CorrespondenceEstimationNormalShooting<PointT, PointT, PointT>::Ptr ce (new registration::CorrespondenceEstimationNormalShooting<PointT, PointT, PointT>);
reg.setCorrespondenceEstimation (ce);
// Add rejector
registration::CorrespondenceRejectorSurfaceNormal::Ptr rej (new registration::CorrespondenceRejectorSurfaceNormal);
rej->setThreshold (0); //Could be a lot of rotation -- just make sure they're at least within 0 degrees
reg.addCorrespondenceRejector (rej);
// Register
reg.align (output);
EXPECT_EQ (int (output.points.size ()), int (cloud_source.points.size ()));
EXPECT_LT (reg.getFitnessScore (), 0.005);
// Check again, for all possible caching schemes
for (int iter = 0; iter < 4; iter++)
{
bool force_cache = (bool) iter/2;
bool force_cache_reciprocal = (bool) iter%2;
pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>);
// Ensure that, when force_cache is not set, we are robust to the wrong input
if (force_cache)
tree->setInputCloud (tgt);
reg.setSearchMethodTarget (tree, force_cache);
pcl::search::KdTree<PointT>::Ptr tree_recip (new pcl::search::KdTree<PointT>);
if (force_cache_reciprocal)
tree_recip->setInputCloud (src);
reg.setSearchMethodSource (tree_recip, force_cache_reciprocal);
// Register
reg.align (output);
EXPECT_EQ (int (output.points.size ()), int (cloud_source.points.size ()));
EXPECT_LT (reg.getFitnessScore (), 0.005);
}
// We get different results on 32 vs 64-bit systems. To address this, we've removed the explicit output test
// on the transformation matrix. Instead, we're testing to make sure the algorithm converges to a sufficiently
// low error by checking the fitness score.
/*
Eigen::Matrix4f transformation = reg.getFinalTransformation ();
EXPECT_NEAR (transformation (0, 0), 0.9046, 1e-2);
EXPECT_NEAR (transformation (0, 1), 0.0609, 1e-2);
EXPECT_NEAR (transformation (0, 2), -0.4219, 1e-2);
EXPECT_NEAR (transformation (0, 3), 0.0327, 1e-2);
EXPECT_NEAR (transformation (1, 0), -0.0328, 1e-2);
EXPECT_NEAR (transformation (1, 1), 0.9968, 1e-2);
EXPECT_NEAR (transformation (1, 2), 0.0851, 1e-2);
EXPECT_NEAR (transformation (1, 3), -0.0003, 1e-2);
EXPECT_NEAR (transformation (2, 0), 0.4250, 1e-2);
EXPECT_NEAR (transformation (2, 1), -0.0641, 1e-2);
EXPECT_NEAR (transformation (2, 2), 0.9037, 1e-2);
EXPECT_NEAR (transformation (2, 3), 0.0413, 1e-2);
EXPECT_EQ (transformation (3, 0), 0);
EXPECT_EQ (transformation (3, 1), 0);
EXPECT_EQ (transformation (3, 2), 0);
EXPECT_EQ (transformation (3, 3), 1);
*/
}