Plane Model Segmentation

This tutorial shows how to use these optimizations inside a Docker* image. For the same functionality outside of Docker* images, see PCL Optimizations Outside of Docker* Images.

1. Prepare the environment:

   cd <edge_insights_for_amr_path>/Edge_Insights_for_Autonomous_Mobile_Robots_<version>/AMR_containers
   ./run_interactive_docker.sh eiforamr-full-flavour-sdk:2023.1 root -c full_flavor
   mkdir one_api_segmentation && cd one_api_segmentation
   

2. Create the file oneapi_segmentation.cpp:

   vim oneapi_segmentation.cpp
   

3. Place the following inside the file:

   #include <pcl/oneapi/segmentation/segmentation.h>
   #include <pcl/io/pcd_io.h>
   #include <pcl/point_types.h>
   #include <pcl/pcl_config.h>
   
   int main (int argc, char **argv)
   {
       //Read Point Cloud
       pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_input (new pcl::PointCloud<pcl::PointXYZ> ());
   
       //Load a standard PCD file from disk
       int result = pcl::io::loadPCDFile(argv[1], *cloud_input);
       if (result != 0)
       {
           pcl::console::print_info ("Load pcd file failed.\n");
           return result;
       }
   
       //Create the oneapi_segmentation object
       pcl::oneapi::SACSegmentation seg;
   
       //Configure oneapi_segmentation class 
       seg.setInputCloud(cloud_input);
       seg.setProbability(0.99);
       seg.setMaxIterations(50);
       seg.setDistanceThreshold(0.01);
       //Optional  
       seg.setOptimizeCoefficients(true);
       //Set algorithm method and model type 
       seg.setMethodType(pcl::oneapi::SAC_RANSAC);
       seg.setModelType (pcl::oneapi::SACMODEL_PLANE);
   
       //Out parameter declaration for getting inliers and model coefficients  
       pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
       double coeffs[4]={0,0,0,0};
       
       //Getting inliers and model coefficients
       seg.segment(*inliers, coeffs);
   
       std::cout << "input cloud size   : " << seg.getCloudSize() << std::endl;
       std::cout << "inliers size       : " << seg.getInliersSize() << std::endl;
       std::cout << "model coefficients : " << coeffs[0] << ", " << coeffs[1] << ", " << coeffs[2] << ", " << coeffs[3] << std::endl;
   
       return 0;
   }
   

4. Create a CMakeLists.txt file:

   vim CMakeLists.txt
   

5. Place the following inside the file:

   cmake_minimum_required(VERSION 3.5 FATAL_ERROR)
   set(target oneapi_segmentation)
   set(CMAKE_CXX_COMPILER dpcpp)
   set(CMAKE_CXX_STANDARD 17)
   set(CMAKE_CXX_FLAGS "-Wall -Wpedantic -Wno-unknown-pragmas -Wno-pass-failed -Wno-unneeded-internal-declaration -Wno-unused-function -Wno-gnu-anonymous-struct -Wno-nested-anon-types -Wno-extra-semi -Wno-unused-local-typedef -fsycl -fsycl-unnamed-lambda -ferror-limit=1")
   project(${target})
   
   find_package(PCL 1.12 REQUIRED)
   find_package(PCL-ONEAPI 1.12 REQUIRED)
   
   include_directories(${PCL_INCLUDE_DIRS} ${PCL-ONEAPI_INCLUDE_DIRS})
   link_directories(${PCL_LIBRARY_DIRS} ${PCL-ONEAPI_LIBRARY_DIRS})
   add_definitions(${PCL_DEFINITIONS} ${PCL-ONEAPI_DEFINITIONS})
   
   add_executable (${target} oneapi_segmentation.cpp)
   target_link_libraries (${target} sycl pcl_oneapi_containers pcl_oneapi_segmentation ${PCL_LIBRARIES})
   

6. Source the Intel® oneAPI Base Toolkit environment:

   export PATH=/home/eiforamr/workspace/lib/pcl/share/pcl-1.12:/home/eiforamr/workspace/lib/pcl/share/pcl-oneapi-1.12:$PATH
   source /opt/intel/oneapi/setvars.sh
   

7. Build the code:

   cd /home/eiforamr/workspace/one_api_segmentation/
   mkdir build && cd build
   cmake ../
   make -j
   

8. Download the test data from GitHub*:

   wget https://raw.githubusercontent.com/PointCloudLibrary/data/5c26bdd0591ba150b91858b5c9fe5e91cb39ae86/segmentation/mOSD/test/test59.pcd
   # if the binary is not downloaded try setting the proxies first and try again:
   export http_proxy="http://<http_proxy>:port"
   export https_proxy="http://<https_proxy>:port"
   

9. Run the binary:

   ./oneapi_segmentation ./test59.pcd
   

Expected results example:

input cloud size   : 307200
inliers size       : 25332
model coefficients : -0.176599, -1.87228, -1.08408, 1

Code Explanation

Load the test data from GitHub* into a PointCloud<PointXYZ>.

    //Read Point Cloud
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_input (new pcl::PointCloud<pcl::PointXYZ> ());

Create the oneapi_segmentation object.

    pcl::oneapi::SACSegmentation seg;

Configure the oneapi_segmentation class.

    seg.setInputCloud(cloud_input);
    seg.setProbability(0.99);
    seg.setMaxIterations(50);
    seg.setDistanceThreshold(0.01);
    //Optional  
    seg.setOptimizeCoefficients(true);
    //Set algorithm method and model type 
    seg.setMethodType(pcl::oneapi::SAC_RANSAC);
    seg.setModelType (pcl::oneapi::SACMODEL_PLANE);

Set to true if a coefficient refinement is required.

    seg.setOptimizeCoefficients(true);

Set the algorithm method and model type.

    seg.setMethodType(pcl::oneapi::SAC_RANSAC);
    seg.setModelType (pcl::oneapi::SACMODEL_PLANE);

Declare output parameters for getting inliers and model coefficients.

    pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
    double coeffs[4]={0,0,0,0};

Get inliers and model coefficients by calling the segment() API.

    seg.segment(*inliers, coeffs);

Result (output log):

    std::cout << "input cloud size   : " << seg.getCloudSize() << std::endl;
    std::cout << "inliers size       : " << seg.getInliersSize() << std::endl;
    std::cout << "model coefficients : " << coeffs[0] << ", " << coeffs[1] << ", " << coeffs[2] << ", " << coeffs[3] << std::endl;