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C++ PCL点云配准源码实例
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前言
这篇博客针对《C++ PCL点云配准源码实例》编写代码，代码整洁，规则，易读。 学习与应用推荐首选。
运行结果
文章目录
一、所需工具软件
二、使用步骤
1. 主要代码
2. 运行结果
三、在线协助
一、所需工具软件
1. VS2019
2. C++
二、使用步骤
代码如下（示例）：
#include <pcl/registration/ia_ransac.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/fpfh.h>
#include <pcl/search/kdtree.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/voxel_grid.h>
#include <time.h>
using namespace std;
//点云可视化
void visualize_pcd(PointCloud::Ptr pcd_src, PointCloud::Ptr pcd_tgt, PointCloud::Ptr pcd_final)
{
pcl::visualization::PCLVisualizer viewer("registration Viewer");
pcl::visualization::PointCloudColorHandlerCustom<PointT> final_h(pcd_final, 0, 0, 255);
viewer.addPointCloud(pcd_src, src_h, "source cloud");
viewer.addPointCloud(pcd_tgt, tgt_h, "tgt cloud");
viewer.addPointCloud(pcd_final, final_h, "final cloud");
viewer.addCoordinateSystem(1.0);
while (!viewer.wasStopped())
{
viewer.spinOnce(100);
//boost::this_thread::sleep(boost::posix_time::microseconds(100000));
}
}
//由旋转平移矩阵计算旋转角度
void matrix2angle(Eigen::Matrix4f& result_trans, Eigen::Vector3f& result_angle)
{
double ax, ay, az;
if (result_trans(2, 0) == 1 || result_trans(2, 0) == -1)
{
az = 0;
double dlta;
dlta = atan2(result_trans(0, 1), result_trans(0, 2));
if (result_trans(2, 0) == -1)
{
ay = M_PI / 2;
}
else
{
ay = -M_PI / 2;
}
}
else
{
ay = -asin(result_trans(2, 0));
ax = atan2(result_trans(2, 1) / cos(ay), result_trans(2, 2) / cos(ay));
}
result_angle << ax, ay, az;
}
int
main(int argc, char** argv)
{
//1. 加载点云文件
PointCloud::Ptr cloud_src_o(new PointCloud); //源点云，待配准
pcl::io::loadPCDFile("rabbit_source.pcd", *cloud_src_o);
PointCloud::Ptr cloud_tgt_o(new PointCloud);//目标点云
pcl::io::loadPCDFile("rabbit_target.pcd", *cloud_tgt_o);
cout << "读入点云完成！" << endl;
PointCloud::Ptr cloud_src(new PointCloud);
pcl::VoxelGrid<PointT> voxel_grid;
voxel_grid.setLeafSize(0.3, 0.3, 0.3);
voxel_grid.setInputCloud(cloud_src_o);
cout << "down size *cloud_src_o from " << cloud_src_o->size() << "to" << cloud_src->size() << endl;
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne_src;
ne_src.setInputCloud(cloud_src);
pcl::search::KdTree< pcl::PointXYZ>::Ptr tree_src(new pcl::search::KdTree< pcl::PointXYZ>());
ne_src.setSearchMethod(tree_src);
PointCloud::Ptr cloud_tgt(new PointCloud);
pcl::VoxelGrid<pcl::PointXYZ> voxel_grid_2;
voxel_grid_2.setLeafSize(0.3, 0.3, 0.3);
voxel_grid_2.setInputCloud(cloud_tgt_o);
voxel_grid_2.filter(*cloud_tgt);
cout << "down size *cloud_tgt_o.pcd from " << cloud_tgt_o->size() << "to" << cloud_tgt->size() << endl;
//6.对目标点云进行法线估计
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne_tgt;
ne_tgt.setInputCloud(cloud_tgt);
pcl::search::KdTree< pcl::PointXYZ>::Ptr tree_tgt(new pcl::search::KdTree< pcl::PointXYZ>());
ne_tgt.setSearchMethod(tree_tgt);
pcl::PointCloud<pcl::Normal>::Ptr cloud_tgt_normals(new pcl::PointCloud< pcl::Normal>);
//ne_tgt.setKSearch(20);
ne_tgt.setRadiusSearch(0.02);
ne_tgt.compute(*cloud_tgt_normals);
pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh_tgt;
fpfh_tgt.setInputCloud(cloud_tgt);
fpfh_tgt.setInputNormals(cloud_tgt_normals);
pcl::search::KdTree<PointT>::Ptr tree_tgt_fpfh(new pcl::search::KdTree<PointT>);
//SAC配准
pcl::SampleConsensusInitialAlignment<pcl::PointXYZ, pcl::PointXYZ, pcl::FPFHSignature33> scia;
scia.setInputSource(cloud_src);
scia.setInputTarget(cloud_tgt);
//scia.setMinSampleDistance(1);
//scia.setNumberOfSamples(2);
//scia.setCorrespondenceRandomness(20);
PointCloud::Ptr sac_result(new PointCloud);
scia.align(*sac_result);
cout << "sac has converged:" << scia.hasConverged() << " score: " << scia.getFitnessScore() << endl;
Eigen::Matrix4f sac_trans;
sac_trans = scia.getFinalTransformation();
cout << sac_trans << endl;
//icp配准
PointCloud::Ptr icp_result(new PointCloud);
pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
//Set the max correspondence distance to 4cm (e.g., correspondences with higher distances will be ignored)
icp.setMaxCorrespondenceDistance(0.04);
// 最大迭代次数
icp.setMaximumIterations(50);
// 两次变化矩阵之间的差值
icp.setTransformationEpsilon(1e-10);
cout << "ICP has converged:" << icp.hasConverged()
<< " score: " << icp.getFitnessScore() << endl;
Eigen::Matrix4f icp_trans;
icp_trans = icp.getFinalTransformation();
cout << icp_trans << endl;
//使用创建的变换对未过滤的输入点云进行变换
pcl::transformPointCloud(*cloud_src_o, *icp_result, icp_trans);
//计算误差
Eigen::Vector3f ANGLE_origin;
ANGLE_origin << 0, 0, M_PI / 5;
double error_x, error_y, error_z;
matrix2angle(icp_trans, ANGLE_result);
error_x = fabs(ANGLE_result(0)) - fabs(ANGLE_origin(0));
error_y = fabs(ANGLE_result(1)) - fabs(ANGLE_origin(1));
error_z = fabs(ANGLE_result(2)) - fabs(ANGLE_origin(2));
//可视化
visualize_pcd(cloud_src_o, cloud_tgt_o, icp_result);
return (0);
}
运行结果
三、在线协助：
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