用PCL写一个fpfh+icp的点云配准算法

好的，下面是基于PCL库的fpfh+icp点云配准算法的示例代码：

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/icp.h>
#include <pcl/features/fpfh.h>
#include <pcl/visualization/pcl_visualizer.h>

int main(int argc, char** argv)
{
    // Load point clouds
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_source(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_target(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::io::loadPCDFile<pcl::PointXYZ>("cloud_source.pcd", *cloud_source);
    pcl::io::loadPCDFile<pcl::PointXYZ>("cloud_target.pcd", *cloud_target);

    // Compute FPFH features
    pcl::FPFHEstimation<pcl::PointXYZ, pcl::FPFHSignature33> fpfh;
    fpfh.setInputCloud(cloud_source);
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
    fpfh.setSearchMethod(tree);
    pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs_source(new pcl::PointCloud<pcl::FPFHSignature33>);
    fpfh.setRadiusSearch(0.05); // Set the radius of the sphere for local feature extraction
    fpfh.compute(*fpfhs_source);
    fpfh.setInputCloud(cloud_target);
    pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs_target(new pcl::PointCloud<pcl::FPFHSignature33>);
    fpfh.compute(*fpfhs_target);

    // Perform ICP registration
    pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
    icp.setInputCloud(cloud_source);
    icp.setInputTarget(cloud_target);
    icp.setMaxCorrespondenceDistance(0.1); // Set the maximum distance between correspondences (corresponding points) in two point clouds
    icp.setMaximumIterations(100); // Set the maximum number of iterations
    icp.setTransformationEpsilon(1e-8); // Set the transformation epsilon (minimum difference between two consecutive transformations)
    icp.setEuclideanFitnessEpsilon(1e-5); // Set the fitness epsilon (minimum difference between two consecutive fitness scores)
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_source_registered(new pcl::PointCloud<pcl::PointXYZ>);
    icp.align(*cloud_source_registered, fpfhs_source->points, fpfhs_target->points); // Use FPFH features as additional constraints

    // Show the results
    std::cout << "ICP converged:" << icp.hasConverged() << ", score: " << icp.getFitnessScore() << std::endl;
    std::cout << "Transformation matrix:" << std::endl << icp.getFinalTransformation() << std::endl;
    pcl::visualization::PCLVisualizer viewer("Point Cloud Registration");
    viewer.addPointCloud(cloud_target, "cloud_target");
    viewer.addPointCloud(cloud_source_registered, "cloud_source_registered");
    viewer.spin();

    return 0;
}

上述代码中，先加载点云数据 `cloud_source.pcd` 和 `cloud_target.pcd`，然后使用FPFH特征提取算法获取两个点云的局部特征描述子。接着使用ICP算法进行点云配准，其中将FPFH特征描述子作为额外的约束条件，增强了算法的鲁棒性和精度。最后展示配准结果。

需要注意的是，此处使用了FPFH特征描述子作为约束条件，需要设置合适的半径 `setRadiusSearch`，以控制局部特征描述子的大小。同时还需要设置ICP算法的各个参数，以便获得最优的配准结果。