pcl :: RANSAC细分，获取云中的所有平面？

Question

我有一个Point Cloud Library功能，可以检测点云中最大的平面。 这非常有效。 现在我想扩展此功能以分割云中的每个平面并将这些点复制到新的云中（例如，在房间的地板上有一个球体的场景会让我回到地板和墙壁，但是不是球体，因为它不是平面的）。 我如何扩展下面的代码来获得所有的飞机，而不仅仅是最大的飞机？ （运行时是这里的一个因素，所以我宁愿不在循环中运行相同的代码，每次剥离新的最大平面）

谢谢。

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);
}

Answer 1

获得第一个平面后，移除这些点并使用算法计算一个新平面，直到估计平面上没有剩余点为止。 第二种情况是因为使用RANSAC，只要有足够的点，你总能找到一架飞机。 我在这里做了类似的事情（这是一个ros节点的回调）：

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);
}

你可以在这里找到整个节点

Answer 2

为此我创建了这个方法：

1）找到第一架飞机

2）获得点云中靠近平面点的所有点

3）使用线性回归通过平面近似该点

4）重复1-3直到新平面不太小