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Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms
Occupancy mapping is widely used to generate volumetric 3D environment models from point clouds, informing a robotic platform which parts of the environment are free and which are not. The selection of the parameters that govern the point cloud generation algorithms and mapping algorithms affects th...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588047/ https://www.ncbi.nlm.nih.gov/pubmed/34770311 http://dx.doi.org/10.3390/s21217004 |
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author | Miao, Yu Hunter, Alan Georgilas, Ioannis |
author_facet | Miao, Yu Hunter, Alan Georgilas, Ioannis |
author_sort | Miao, Yu |
collection | PubMed |
description | Occupancy mapping is widely used to generate volumetric 3D environment models from point clouds, informing a robotic platform which parts of the environment are free and which are not. The selection of the parameters that govern the point cloud generation algorithms and mapping algorithms affects the process and the quality of the final map. Although previous studies have been reported in the literature on optimising major parameter configurations, research in the process to identify optimal parameter sets to achieve best occupancy mapping performance remains limited. The current work aims to fill this gap with a two-step principled methodology that first identifies the most significant parameters by conducting Neighbourhood Component Analysis on all parameters and then optimise those using grid search with the area under the Receiver Operating Characteristic curve. This study is conducted on 20 data sets with specially designed targets, providing precise ground truths for evaluation purposes. The methodology is tested on OctoMap with point clouds created by applying StereoSGBM on the images from a stereo camera. A clear indication can be seen that mapping parameters are more important than point cloud generation parameters. Moreover, up to 15% improvement in mapping performance can be achieved over default parameters. |
format | Online Article Text |
id | pubmed-8588047 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85880472021-11-13 Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms Miao, Yu Hunter, Alan Georgilas, Ioannis Sensors (Basel) Article Occupancy mapping is widely used to generate volumetric 3D environment models from point clouds, informing a robotic platform which parts of the environment are free and which are not. The selection of the parameters that govern the point cloud generation algorithms and mapping algorithms affects the process and the quality of the final map. Although previous studies have been reported in the literature on optimising major parameter configurations, research in the process to identify optimal parameter sets to achieve best occupancy mapping performance remains limited. The current work aims to fill this gap with a two-step principled methodology that first identifies the most significant parameters by conducting Neighbourhood Component Analysis on all parameters and then optimise those using grid search with the area under the Receiver Operating Characteristic curve. This study is conducted on 20 data sets with specially designed targets, providing precise ground truths for evaluation purposes. The methodology is tested on OctoMap with point clouds created by applying StereoSGBM on the images from a stereo camera. A clear indication can be seen that mapping parameters are more important than point cloud generation parameters. Moreover, up to 15% improvement in mapping performance can be achieved over default parameters. MDPI 2021-10-22 /pmc/articles/PMC8588047/ /pubmed/34770311 http://dx.doi.org/10.3390/s21217004 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Miao, Yu Hunter, Alan Georgilas, Ioannis Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms |
title | Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms |
title_full | Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms |
title_fullStr | Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms |
title_full_unstemmed | Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms |
title_short | Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms |
title_sort | parameter reduction and optimisation for point cloud and occupancy mapping algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588047/ https://www.ncbi.nlm.nih.gov/pubmed/34770311 http://dx.doi.org/10.3390/s21217004 |
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