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Fixing windows pcl link error

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matlabbe
2026-05-28 21:21:14 -07:00
parent 875de7ae53
commit 10a3949b6a
1 changed files with 38 additions and 21 deletions
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corelib/src/util3d_registration.cpp
+38 -21
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@@ -33,6 +33,18 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <pcl/registration/correspondence_rejection_sample_consensus.h>
#include <pcl/registration/icp.h>
// Explicitly instantiate pcl::RandomSampleConsensus and
// pcl::SampleConsensusModelRegistration for pcl::PointXYZINormal. PCL itself
// only ships precompiled symbols for the types listed in PCL_XYZ_POINT_TYPES
// when built without PCL_ONLY_CORE_POINT_TYPES; the Windows pre-built PCL
// (and any "core point types" build) omits PointXYZINormal, so without this
// rtabmap_core.dll fails to link when the rejector is used with that type.
#include <pcl/sample_consensus/impl/ransac.hpp>
#include <pcl/sample_consensus/impl/sac_model_registration.hpp>
template class pcl::RandomSampleConsensus<pcl::PointXYZINormal>;
template class pcl::SampleConsensusModelRegistration<pcl::PointXYZINormal>;
#include <pcl/registration/transformation_estimation_2D.h>
#include <pcl/registration/transformation_estimation_svd.h>
#include <pcl/sample_consensus/sac_model_registration.h>
@@ -394,6 +406,28 @@ void computeVarianceAndCorrespondences(
computeVarianceAndCorrespondencesImpl<pcl::PointXYZI>(cloudA, cloudB, maxCorrespondenceDistance, variance, correspondencesOut, reciprocal);
}
// RANSAC-based correspondence rejector: fits a rigid transform on random
// 3-pair subsets and discards pairs that disagree. PCL's
// IterativeClosestPoint::setRANSACOutlierRejectionThreshold and
// setRANSACIterations are NOT honored by ICP itself (only by NDT and
// k-4PCS), so we install the rejector explicitly here.
template<typename PointT>
void addRansacRejector(
pcl::IterativeClosestPoint<PointT, PointT> & icp,
const typename pcl::PointCloud<PointT>::ConstPtr & cloud_source,
const typename pcl::PointCloud<PointT>::ConstPtr & cloud_target,
double maxCorrespondenceDistance,
float ransacOutlierRatio)
{
typename pcl::registration::CorrespondenceRejectorSampleConsensus<PointT>::Ptr
rejector(new pcl::registration::CorrespondenceRejectorSampleConsensus<PointT>());
rejector->setInlierThreshold(maxCorrespondenceDistance * ransacOutlierRatio);
rejector->setMaximumIterations(50);
rejector->setInputSource(cloud_source);
rejector->setInputTarget(cloud_target);
icp.addCorrespondenceRejector(rejector);
}
// return transform from source to target (All points must be finite!!!)
template<typename PointT>
Transform icpImpl(const typename pcl::PointCloud<PointT>::ConstPtr & cloud_source,
@@ -429,18 +463,8 @@ Transform icpImpl(const typename pcl::PointCloud<PointT>::ConstPtr & cloud_sourc
if(ransacOutlierRatio > 0.0f && ransacOutlierRatio < 1.0f)
{
// RANSAC-based correspondence rejector: fits a rigid transform on
// random 3-pair subsets and discards pairs that disagree. Note that
// PCL's IterativeClosestPoint::setRANSACOutlierRejectionThreshold and
// setRANSACIterations are NOT honored by ICP itself (only by NDT and
// k-4PCS), so we install the rejector explicitly.
typename pcl::registration::CorrespondenceRejectorSampleConsensus<PointT>::Ptr
ransacRejector(new pcl::registration::CorrespondenceRejectorSampleConsensus<PointT>());
ransacRejector->setInlierThreshold(maxCorrespondenceDistance * ransacOutlierRatio);
ransacRejector->setMaximumIterations(50);
ransacRejector->setInputSource(cloud_source);
ransacRejector->setInputTarget(cloud_target);
icp.addCorrespondenceRejector(ransacRejector);
addRansacRejector<PointT>(icp, cloud_source, cloud_target,
maxCorrespondenceDistance, ransacOutlierRatio);
}
// Perform the alignment
@@ -510,15 +534,8 @@ Transform icpPointToPlaneImpl(
if(ransacOutlierRatio > 0.0f && ransacOutlierRatio < 1.0f)
{
// See icpImpl: install a RANSAC correspondence rejector since PCL's
// setRANSAC*-on-ICP is a no-op.
typename pcl::registration::CorrespondenceRejectorSampleConsensus<PointNormalT>::Ptr
ransacRejector(new pcl::registration::CorrespondenceRejectorSampleConsensus<PointNormalT>());
ransacRejector->setInlierThreshold(maxCorrespondenceDistance * ransacOutlierRatio);
ransacRejector->setMaximumIterations(50);
ransacRejector->setInputSource(cloud_source);
ransacRejector->setInputTarget(cloud_target);
icp.addCorrespondenceRejector(ransacRejector);
addRansacRejector<PointNormalT>(icp, cloud_source, cloud_target,
maxCorrespondenceDistance, ransacOutlierRatio);
}
// Perform the alignment