pcl::RANSAC segmentation, get all planes in cloud?

Question

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);
}

Answer 1

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);
}

you can find the whole node here