问题描述

我有一个点云库函数，可以检测点云中最大的平面.这很好用.现在，我想扩展此功能以分割出云中的每个平面并将这些点复制到新的云中(例如，房间地板上有球体的场景将返回地板和墙壁，但不是球体，因为它不是平面的).如何扩展以下代码以获取所有飞机，而不仅仅是最大的飞机?(运行时间是这里的一个因素，所以我不希望只是在循环中运行相同的代码，每次都剥离新的最大平面)

I have a Point Cloud Library function that detects the largest plane in a point cloud. This works great. Now, I would like to extend this functionality to segment out every planar surface in the cloud and copy those points to a new cloud (for example, a scene with a sphere on the floor of a room would give me back the floor and walls, but not the sphere, as it is not planar). How can I extend the below code to get all the planes, not just the largest one? (runtime is a factor here, so I would prefer not to just run this same code in a loop, stripping out the new largest plane each time)

int
main(int argc, char** argv)
{
    pcl::visualization::CloudViewer viewer("viewer1");

    pcl::PCLPointCloud2::Ptr cloud_blob(new pcl::PCLPointCloud2), cloud_filtered_blob(new pcl::PCLPointCloud2);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>), cloud_p(new pcl::PointCloud<pcl::PointXYZ>), cloud_f(new pcl::PointCloud<pcl::PointXYZ>);

    // Fill in the cloud data
    pcl::PCDReader reader;
    reader.read("clouds/table.pcd", *cloud_blob);

    // Create the filtering object: downsample the dataset using a leaf size of 1cm
    pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
    sor.setInputCloud(cloud_blob);
    sor.setLeafSize(0.01f, 0.01f, 0.01f);
    sor.filter(*cloud_filtered_blob);

    // Convert to the templated PointCloud
    pcl::fromPCLPointCloud2(*cloud_filtered_blob, *cloud_filtered);

    std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height << " data points." << std::endl;

    pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients());
    pcl::PointIndices::Ptr inliers(new pcl::PointIndices());
    // Create the segmentation object
    pcl::SACSegmentation<pcl::PointXYZ> seg;
    // Optional
    seg.setOptimizeCoefficients(true);
    seg.setModelType(pcl::SACMODEL_PLANE);
    seg.setMethodType(pcl::SAC_RANSAC);
    seg.setMaxIterations(1000);
    seg.setDistanceThreshold(0.01);

    // Create the filtering object
    pcl::ExtractIndices<pcl::PointXYZ> extract;

    int i = 0, nr_points = (int)cloud_filtered->points.size();
    // While 30% of the original cloud is still there
    while (cloud_filtered->points.size() > 0.3 * nr_points)
    {
        // Segment the largest planar component from the remaining cloud
        seg.setInputCloud(cloud_filtered);
        pcl::ScopeTime scopeTime("Test loop");
        {
            seg.segment(*inliers, *coefficients);
        }
        if (inliers->indices.size() == 0)
        {
            std::cerr << "Could not estimate a planar model for the given dataset." << std::endl;
            break;
        }

        // Extract the inliers
        extract.setInputCloud(cloud_filtered);
        extract.setIndices(inliers);
        extract.setNegative(false);
        extract.filter(*cloud_p);
        std::cerr << "PointCloud representing the planar component: " << cloud_p->width * cloud_p->height << " data points." << std::endl;

        }

    viewer.showCloud(cloud_p, "viewer1");
    while (!viewer.wasStopped()) {}

    return (0);
}

推荐答案

一旦你得到第一个平面，删除点并使用算法计算一个新平面，直到估计平面没有剩下的点不是这样事情了.第二种情况是因为使用 RANSAC，只要有足够的点，您总会找到一个平面.我在这里做了类似的事情(这是一个 ros 节点的回调):

Once you get the first plane, remove the points and use the algorithm to compute a new plane until either there are no points left of the estimated plane is no such thing anymore. The second case is because using RANSAC you will always find a plane as long as there are enough points. I have something similar done here (this is a callback for a ros node):

void pointCloudCb(const sensor_msgs::PointCloud2::ConstPtr &msg){

    // Convert to pcl point cloud
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_msg (new pcl::PointCloud<pcl::PointXYZ>);
    pcl::fromROSMsg(*msg,*cloud_msg);
    ROS_DEBUG("%s: new ponitcloud (%i,%i)(%zu)",_name.c_str(),cloud_msg->width,cloud_msg->height,cloud_msg->size());

    // Filter cloud
    pcl::PassThrough<pcl::PointXYZ> pass;
    pass.setInputCloud(cloud_msg);
    pass.setFilterFieldName ("z");
    pass.setFilterLimits(0.001,10000);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
    pass.filter (*cloud);

    // Get segmentation ready
    pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
    pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
    pcl::SACSegmentation<pcl::PointXYZ> seg;
    pcl::ExtractIndices<pcl::PointXYZ> extract;
    seg.setOptimizeCoefficients (true);
    seg.setModelType (pcl::SACMODEL_PLANE);
    seg.setMethodType (pcl::SAC_RANSAC);
    seg.setDistanceThreshold(_max_distance);

    // Create pointcloud to publish inliers
    pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_pub(new pcl::PointCloud<pcl::PointXYZRGB>);
    int original_size(cloud->height*cloud->width);
    int n_planes(0);
    while (cloud->height*cloud->width>original_size*_min_percentage/100){

        // Fit a plane
        seg.setInputCloud(cloud);
        seg.segment(*inliers, *coefficients);

        // Check result
        if (inliers->indices.size() == 0)
            break;

        // Iterate inliers
        double mean_error(0);
        double max_error(0);
        double min_error(100000);
        std::vector<double> err;
        for (int i=0;i<inliers->indices.size();i++){

            // Get Point
            pcl::PointXYZ pt = cloud->points[inliers->indices[i]];

            // Compute distance
            double d = point2planedistnace(pt,coefficients)*1000;// mm
            err.push_back(d);

            // Update statistics
            mean_error += d;
            if (d>max_error) max_error = d;
            if (d<min_error) min_error = d;

        }
        mean_error/=inliers->indices.size();

        // Compute Standard deviation
        ColorMap cm(min_error,max_error);
        double sigma(0);
        for (int i=0;i<inliers->indices.size();i++){

            sigma += pow(err[i] - mean_error,2);

            // Get Point
            pcl::PointXYZ pt = cloud->points[inliers->indices[i]];

            // Copy point to noew cloud
            pcl::PointXYZRGB pt_color;
            pt_color.x = pt.x;
            pt_color.y = pt.y;
            pt_color.z = pt.z;
            uint32_t rgb;
            if (_color_pc_with_error)
                rgb = cm.getColor(err[i]);
            else
                rgb = colors[n_planes].getColor();
            pt_color.rgb = *reinterpret_cast<float*>(&rgb);
            cloud_pub->points.push_back(pt_color);

        }
        sigma = sqrt(sigma/inliers->indices.size());

        // Extract inliers
        extract.setInputCloud(cloud);
        extract.setIndices(inliers);
        extract.setNegative(true);
        pcl::PointCloud<pcl::PointXYZ> cloudF;
        extract.filter(cloudF);
        cloud->swap(cloudF);

        // Display infor
        ROS_INFO("%s: fitted plane %i: %fx%s%fy%s%fz%s%f=0 (inliers: %zu/%i)",
                 _name.c_str(),n_planes,
                 coefficients->values[0],(coefficients->values[1]>=0?"+":""),
                 coefficients->values[1],(coefficients->values[2]>=0?"+":""),
                 coefficients->values[2],(coefficients->values[3]>=0?"+":""),
                 coefficients->values[3],
                 inliers->indices.size(),original_size);
        ROS_INFO("%s: mean error: %f(mm), standard deviation: %f (mm), max error: %f(mm)",_name.c_str(),mean_error,sigma,max_error);
        ROS_INFO("%s: poitns left in cloud %i",_name.c_str(),cloud->width*cloud->height);

        // Nest iteration
        n_planes++;
    }

    // Publish points
    sensor_msgs::PointCloud2 cloud_publish;
    pcl::toROSMsg(*cloud_pub,cloud_publish);
    cloud_publish.header = msg->header;
    _pub_inliers.publish(cloud_publish);
}

你可以找到整个节点这里

这篇关于pcl::RANSAC 分割，获取云中的所有平面?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持编程学习网！