PCL_三维点云拼接融合/点云粗配准/点云精配准

妖狐艹你老母 2022-12-25 04:58 865阅读 0赞

# 1.点云粗配准拼接-Ransac #

#include <pcl/common/transforms.h>
    #include <pcl/console/parse.h>
    #include <pcl/console/time.h>
    #include <pcl/point_types.h>
    #include <pcl/io/pcd_io.h>
    #include <pcl/sample_consensus/method_types.h>
    #include <pcl/sample_consensus/ransac.h>
    #include <pcl/sample_consensus/sac_model_registration.h>
     
    using namespace pcl;
     
    //
    void
        compute (const PointCloud<PointXYZ>::Ptr &input, 
        const PointCloud<PointXYZ>::Ptr &target,
        Eigen::Matrix4f &transformation,
        const double thresh)
    {
        SampleConsensusModelRegistration<PointXYZ>::Ptr model (new SampleConsensusModelRegistration<PointXYZ> (input));
        model->setInputTarget (target);
     
        RandomSampleConsensus<PointXYZ> sac (model, thresh);
        sac.setMaxIterations (100000);
     
        if (!sac.computeModel (2))
        {
            PCL_ERROR ("Could not compute a valid transformation!\n");
            return;
        }
        Eigen::VectorXf coeff;
        sac.getModelCoefficients (coeff);
        transformation.row (0) = coeff.segment<4>(0);
        transformation.row (1) = coeff.segment<4>(4);
        transformation.row (2) = coeff.segment<4>(8);
        transformation.row (3) = coeff.segment<4>(12);
    }
     
    ///
    int
        main (int argc, char** argv)
    {
        PointCloud<PointXYZ>::Ptr source (new PointCloud<PointXYZ>);
        PointCloud<PointXYZ>::Ptr target (new PointCloud<PointXYZ>);
        io::loadPCDFile ("1.pcd", *source);
        io::loadPCDFile ("2.pcd", *target);
     
         // Compute
        Eigen::Matrix4f transform;
        double thresh = 0.002;
        compute (source, target, transform, thresh);
     
        PointCloud<PointXYZ> output;
        transformPointCloud (*source, output, transform);
        output = output+*target;
        io::savePCDFileASCII ("result.pcd", output);
     
        return (0);
    }

# 2.点云精配准 #

**使用 移除NAN点/ 体素过滤/ 计算曲面法线和曲率/ 非线性ICP/**

/* \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;
    //简单类型定义
    typedef pcl::PointXYZ PointT;
    typedef pcl::PointCloud<PointT> PointCloud;
    typedef pcl::PointNormal PointNormalT;
    typedef pcl::PointCloud<PointNormalT> PointCloudWithNormals;
    //这是一个辅助教程，因此我们可以负担全局变量
    //创建可视化工具
    pcl::visualization::PCLVisualizer *p;
    //定义左右视点
    int vp_1, vp_2;
    //处理点云的方便的结构定义
    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);
        }
    };
    //以< x, y, z, curvature >形式定义一个新的点
    class MyPointRepresentation : public pcl::PointRepresentation <PointNormalT>
    {
        using pcl::PointRepresentation<PointNormalT>::nr_dimensions_;
    public:
        MyPointRepresentation ()
        {
            //定义尺寸值
            nr_dimensions_ = 4;
        }
        //覆盖copyToFloatArray方法来定义我们的特征矢量
        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;
        }
    };
     
    /** 在可视化窗口的第一视点显示源点云和目标点云
    *
    */
    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();
    }
     
    /**在可视化窗口的第二视点显示源点云和目标点云
    *
    */
    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();
    }
     
    /**加载一组我们想要匹配在一起的PCD文件
    * 参数argc是参数的数量 (pass from main ())
    *参数 argv 实际的命令行参数 (pass from main ())
    *参数models点云数据集的合成矢量
    */
    void loadData (int argc, char **argv, std::vector<PCD, Eigen::aligned_allocator<PCD> > &models)
    {
        std::string extension (".pcd");
        //假定第一个参数是实际测试模型
        for (int i = 1; i < argc; i++)
        {
            std::string fname = std::string (argv[i]);
            // 至少需要5个字符长（因为.plot就有 5个字符）
            if (fname.size () <= extension.size ())
                continue;
            std::transform (fname.begin (), fname.end (), fname.begin (), (int(*)(int))tolower);
            //检查参数是一个pcd文件
            if (fname.compare (fname.size () - extension.size (), extension.size (), extension) == 0)
            {
                //加载点云并保存在总体的模型列表中
                PCD m;
                m.f_name = argv[i];
                pcl::io::loadPCDFile (argv[i], *m.cloud);
                //从点云中移除NAN点
                std::vector<int> indices;
                pcl::removeNaNFromPointCloud(*m.cloud,*m.cloud, indices);
                models.push_back (m);
            }
        }
    }
     
    /**匹配一对点云数据集并且返还结果
    *参数 cloud_src 是源点云
    *参数 cloud_src 是目标点云
    *参数output输出的配准结果的源点云
    *参数final_transform是在来源和目标之间的转换
    */
    void pairAlign (const PointCloud::Ptr cloud_src, const PointCloud::Ptr cloud_tgt, PointCloud::Ptr output, Eigen::Matrix4f &final_transform, bool downsample = false)
    {
        //
        //为了一致性和高速的下采样
        //注意：为了大数据集需要允许这项
        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;
        }
        //计算曲面法线和曲率
        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);
        //
        //举例说明我们自定义点的表示（以上定义）
        MyPointRepresentation point_representation;
        //调整'curvature'尺寸权重以便使它和x, y, z平衡
        float alpha[4] = {1.0, 1.0, 1.0, 1.0};
        point_representation.setRescaleValues (alpha);
        //
        // 配准
        pcl::IterativeClosestPointNonLinear<PointNormalT, PointNormalT> reg;
        reg.setTransformationEpsilon (1e-6);
        //将两个对应关系之间的(src<->tgt)最大距离设置为10厘米
        //注意：根据你的数据集大小来调整
        reg.setMaxCorrespondenceDistance (0.1);  
        //设置点表示
        reg.setPointRepresentation (boost::make_shared<const MyPointRepresentation> (point_representation));
        reg.setInputCloud (points_with_normals_src);
        reg.setInputTarget (points_with_normals_tgt);
        //
        //在一个循环中运行相同的最优化并且使结果可视化
        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);
            //为了可视化的目的保存点云
            points_with_normals_src = reg_result;
            //估计
            reg.setInputCloud (points_with_normals_src);
            reg.align (*reg_result);
            //在每一个迭代之间累积转换
            Ti = reg.getFinalTransformation () * Ti;
            //如果这次转换和之前转换之间的差异小于阈值
            //则通过减小最大对应距离来改善程序
            if (fabs ((reg.getLastIncrementalTransformation () - prev).sum ()) < reg.getTransformationEpsilon ())
                reg.setMaxCorrespondenceDistance (reg.getMaxCorrespondenceDistance () - 0.001);
            prev = reg.getLastIncrementalTransformation ();
            //可视化当前状态
            showCloudsRight(points_with_normals_tgt, points_with_normals_src);
        }
        //
        // 得到目标点云到源点云的变换
        targetToSource = Ti.inverse();
        //
        //把目标点云转换回源框架
        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");
        //添加源点云到转换目标
        *output += *cloud_src;
        final_transform = targetToSource;
    }
    /* ---[ */
    int main (int argc, char** argv)
    {
        pcl::PointCloud<pcl::PointXYZ>::Ptr target1 (new pcl::PointCloud<pcl::PointXYZ>);
        pcl::PointCloud<pcl::PointXYZ> output;
        pcl::io::loadPCDFile (argv[1], *target1);
        pcl::io::loadPCDFile (argv[2], output);
        // 加载数据
        std::vector<PCD, Eigen::aligned_allocator<PCD> > data;
        PCD m;
        std::vector<int> indices;
        m.cloud = target1;
        //从点云中移除NAN点
        pcl::removeNaNFromPointCloud(*m.cloud,*m.cloud, indices);
        data.push_back (m);
        m.cloud = output.makeShared();
        pcl::removeNaNFromPointCloud(*m.cloud,*m.cloud, indices);
        data.push_back (m);
     
        //检查用户输入
        if (data.empty ())
        {
            return (-1);
        }
        PCL_INFO ("Loaded %d datasets.", (int)data.size ());
        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;
     
            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);
            //把当前的两两配对转换到全局变换
            pcl::transformPointCloud (*temp, *result, GlobalTransform);
            //update the global transform更新全局变换
            GlobalTransform = pairTransform * GlobalTransform;
            //保存配准对，转换到第一个点云框架中
            std::stringstream ss;
            ss << i << ".pcd";
            pcl::io::savePCDFile (ss.str (), *result, true);
        }
    }
    /* ]--- */

# 参考： #

*  [PCL三维点云拼接融合技术][PCL]

[PCL]: https://blog.csdn.net/dcba2014/article/details/71859375?locationNum=3&fps=1