PCL RANSAC — C++

Introduction

RANSAC (Random Sample Consensus) is a widely used algorithm for model fitting in computer vision and robotics applications. It is used to estimate parameters of a mathematical model from a set of observed data that contains outliers, i.e., data points that do not fit the model.

Point Cloud Library (PCL) is a popular open-source library for processing 2D/3D point cloud data. PCL provides an implementation of the RANSAC algorithm that can be used for robust model fitting in PCL applications.

This article will provide an introduction to PCL RANSAC and demonstrate how to use it for plane segmentation in a C++ program.

Requirements

• PCL library
• C++11 or higher

PCL RANSAC for Plane Segmentation

Suppose we have a set of point cloud data containing points that belong to a plane as well as some noise points. Our task is to segment the plane from the noisy data points.

PCL RANSAC can be used to robustly estimate the parameters of the plane model from the point cloud data. The basic workflow of PCL RANSAC for plane segmentation is as follows:

1. Randomly select a minimal subset of points from the input data points that can form a plane
2. Estimate the plane parameters using the selected data points
3. For each data point, compute the distance from the point to the estimated plane
4. Count the number of data points that have a distance to the plane within a given threshold (inliers)
5. If the number of inliers is above a certain threshold, re-estimate the plane using all inliers and terminate. Otherwise, repeat steps 1-5 with a new set of randomly selected data points.

The PCL RANSAC algorithm has multiple parameters that can be set by the user to control the algorithm's behavior. The key parameters are:

• The minimum number of data points required to form a model (default=3)
• The maximum number of iterations (default=1000)
• The threshold distance from a data point to the estimated plane (default=0.01).

Code Example

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); 

// Load point cloud data from file
pcl::io::loadPCDFile<pcl::PointXYZ> ("table_scene_lms400.pcd", *cloud); 

pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());

// Create the segmentation object
pcl::SACSegmentation<pcl::PointXYZ> seg;
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (1000);
seg.setDistanceThreshold (0.01);

// Segment the largest planar component from the input cloud
seg.setInputCloud (cloud);
seg.segment (*inliers, *coefficients); 

// Extract the inliers
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud (cloud);
extract.setIndices (inliers);
extract.setNegative (false);

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane (new pcl::PointCloud<pcl::PointXYZ> ());
extract.filter (*cloud_plane); 

// Save the segmented plane to file
pcl::io::savePCDFileASCII ("table_plane.pcd", *cloud_plane);

The above code demonstrates how to use PCL RANSAC for plane segmentation in a C++ program. The code loads a point cloud data from a PCD file and segments the largest planar component from the input cloud. The segmented plane is then saved to a new PCD file for further processing.

Conclusion

PCL RANSAC is a powerful algorithm for robust model fitting in PCL applications. It can be used to perform plane segmentation, object recognition, and feature extraction from point cloud data. By setting the appropriate algorithm parameters, users can customize the behavior of PCL RANSAC to fit their specific application needs.