Detecting Specific Models and Their Parameters in 3D Point Clouds#

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.

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 sample_consensus && cd sample_consensus

Create the file oneapi_sample_consensus.cpp:
```
vim oneapi_sample_consensus.cpp
```

Place the following inside the file:

/*
 * Software License Agreement (BSD License)
 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2010-2012, Willow Garage, Inc.
 *  Copyright (c) 2014-, Open Perception, Inc.
 *
 *  All rights reserved.
 *
 *  Redistribution and use in source and binary forms, with or without
 *  modification, are permitted provided that the following conditions
 *  are met:
 *
 *   * Redistributions of source code must retain the above copyright
 *     notice, this list of conditions and the following disclaimer.
 *   * Redistributions in binary form must reproduce the above
 *     copyright notice, this list of conditions and the following
 *     disclaimer in the documentation and/or other materials provided
 *     with the distribution.
 *   * Neither the name of the copyright holder(s) nor the names of its
 *     contributors may be used to endorse or promote products derived
 *     from this software without specific prior written permission.
 *
 *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 *  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
 *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
 *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
 *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
 *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 *  POSSIBILITY OF SUCH DAMAGE.
 *
 */

#include <pcl/oneapi/sample_consensus/sac_model_plane.h>
#include <pcl/oneapi/sample_consensus/ransac.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>


int main (int argc, char** argv)
{
  // Read Point Cloud
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_ptr( new pcl::PointCloud<pcl::PointXYZ>() );
  pcl::PointCloud<pcl::PointXYZ>::Ptr convex_ptr;
  int result = pcl::io::loadPCDFile(argv[1], *cloud_ptr);
  if (result != 0)
  {
    pcl::console::print_info ("Load pcd file failed.\n");
    return result;
  }

  // Prepare Device Point Cloud Memory
  pcl::oneapi::SampleConsensusModel::PointCloud_xyz cloud_device_xyz;
  cloud_device_xyz.upload(cloud_ptr->points);
  pcl::oneapi::SampleConsensusModel::PointCloud & cloud_device = (pcl::oneapi::SampleConsensusModel::PointCloud &)cloud_device_xyz;

  // Algorithm tests
  typename pcl::oneapi::SampleConsensusModelPlane::Ptr sac_model (new pcl::oneapi::SampleConsensusModelPlane (cloud_device));
  pcl::oneapi::RandomSampleConsensus sac (sac_model);
  sac.setMaxIterations (10000);
  sac.setDistanceThreshold (0.03);
  result = sac.computeModel ();

  // Best model
  pcl::oneapi::SampleConsensusModelPlane::Indices sample;
  sac.getModel (sample);

  // Coefficient
  pcl::oneapi::SampleConsensusModelPlane::Coefficients coeffs;
  sac.getModelCoefficients (coeffs);

  // Inliers
  pcl::Indices pcl_inliers;
  int inliers_size = sac.getInliersSize ();
  pcl_inliers.resize(inliers_size);

  pcl::oneapi::SampleConsensusModelPlane::IndicesPtr inliers = sac.getInliers ();
  inliers->download(pcl_inliers.data(), 0, inliers_size);

  // Refined coefficient
  pcl::oneapi::SampleConsensusModelPlane::Coefficients coeff_refined;
  sac_model->optimizeModelCoefficients (*cloud_ptr, pcl_inliers, coeffs, coeff_refined);

  // print log
  std::cout << "input cloud size: " << cloud_ptr->points.size() << std::endl;
  std::cout << "inliers size    : " << inliers_size << std::endl;
  std::cout << "  plane model coefficient: " << coeffs[0] << ", " << coeffs[1] << ", " << coeffs[2] << ", " << coeffs[3] << std::endl;
  std::cout << "  Optimized coefficient  : " << coeff_refined[0] << ", " << coeff_refined[1] << ", " << coeff_refined[2] << ", " << coeff_refined[3] << std::endl;
}

Create a CMakeLists.txt file:
```
vim CMakeLists.txt
```

Place the following inside the file:

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)
set(target oneapi_sample_consensus)
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 (${target} oneapi_sample_consensus.cpp)
target_link_libraries (${target} sycl pcl_oneapi_sample_consensus ${PCL_LIBRARIES})

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

Build the code:

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

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"

Run the binary:
```
./oneapi_sample_consensus ./test59.pcd
```

Expected results example:

input cloud size: 307200
inliers size    : 77316
plane model coefficient: -0.0789502, -0.816661, -0.571692, 0.546386
Optimized coefficient  : -0.0722213, -0.818286, -0.570256, 0.547587

Code Explanation#

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

  // Read Point Cloud
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_ptr( new pcl::PointCloud<pcl::PointXYZ>() );
  pcl::PointCloud<pcl::PointXYZ>::Ptr convex_ptr;
  int result = pcl::io::loadPCDFile(argv[1], *cloud_ptr);
  if (result != 0)
  {
    pcl::console::print_info ("Load pcd file failed.\n");
    return result;
  }

Create GPU input and output device arrays, and load point cloud data into the input device array.

  // Prepare Device Point Cloud Memory
  pcl::oneapi::SampleConsensusModel::PointCloud_xyz cloud_device_xyz;
  cloud_device_xyz.upload(cloud_ptr->points);
  pcl::oneapi::SampleConsensusModel::PointCloud & cloud_device = (pcl::oneapi::SampleConsensusModel::PointCloud &)cloud_device_xyz;

GPU: Start computing the model.

  typename pcl::oneapi::SampleConsensusModelPlane::Ptr sac_model (new pcl::oneapi::SampleConsensusModelPlane (cloud_device));
  pcl::oneapi::RandomSampleConsensus sac (sac_model);
  sac.setMaxIterations (10000);
  sac.setDistanceThreshold (0.03);
  result = sac.computeModel ();

Result (best model):

  pcl::oneapi::SampleConsensusModelPlane::Indices sample;
  sac.getModel (sample);

Result (coefficient model):

  pcl::oneapi::SampleConsensusModelPlane::Coefficients coeffs;
  sac.getModelCoefficients (coeffs);

Result (inliers model):

  pcl::Indices pcl_inliers;
  int inliers_size = sac.getInliersSize ();
  pcl_inliers.resize(inliers_size);

  pcl::oneapi::SampleConsensusModelPlane::IndicesPtr inliers = sac.getInliers ();
  inliers->download(pcl_inliers.data(), 0, inliers_size);

Result (refined coefficient model):

  pcl::oneapi::SampleConsensusModelPlane::Coefficients coeff_refined;
  sac_model->optimizeModelCoefficients (*cloud_ptr, pcl_inliers, coeffs, coeff_refined);

Result (output log):

  std::cout << "input cloud size: " << cloud_ptr->points.size() << std::endl;
  std::cout << "inliers size    : " << inliers_size << std::endl;
  std::cout << "  plane model coefficient: " << coeffs[0] << ", " << coeffs[1] << ", " << coeffs[2] << ", " << coeffs[3] << std::endl;
  std::cout << "  Optimized coefficient  : " << coeff_refined[0] << ", " << coeff_refined[1] << ", " << coeff_refined[2] << ", " << coeff_refined[3] << std::endl;

Detecting Specific Models and Their Parameters in 3D Point Clouds

Contents

Detecting Specific Models and Their Parameters in 3D Point Clouds#

Code Explanation#