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...

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Autores principales:	Yu Miao, Alan Hunter, Ioannis Georgilas
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	mapping SLAM data sets for SLAM Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/5eff6a37597a42d782f13cadfebdcbfa
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id	oai:doaj.org-article:5eff6a37597a42d782f13cadfebdcbfa
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spelling	oai:doaj.org-article:5eff6a37597a42d782f13cadfebdcbfa2021-11-11T19:02:42ZParameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms10.3390/s212170041424-8220https://doaj.org/article/5eff6a37597a42d782f13cadfebdcbfa2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7004https://doaj.org/toc/1424-8220Occupancy 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.Yu MiaoAlan HunterIoannis GeorgilasMDPI AGarticlemappingSLAMdata sets for SLAMChemical technologyTP1-1185ENSensors, Vol 21, Iss 7004, p 7004 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mapping SLAM data sets for SLAM Chemical technology TP1-1185
spellingShingle	mapping SLAM data sets for SLAM Chemical technology TP1-1185 Yu Miao Alan Hunter Ioannis Georgilas Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms
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	article
author	Yu Miao Alan Hunter Ioannis Georgilas
author_facet	Yu Miao Alan Hunter Ioannis Georgilas
author_sort	Yu Miao
title	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_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_sort	parameter reduction and optimisation for point cloud and occupancy mapping algorithms
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5eff6a37597a42d782f13cadfebdcbfa
work_keys_str_mv	AT yumiao parameterreductionandoptimisationforpointcloudandoccupancymappingalgorithms AT alanhunter parameterreductionandoptimisationforpointcloudandoccupancymappingalgorithms AT ioannisgeorgilas parameterreductionandoptimisationforpointcloudandoccupancymappingalgorithms
_version_	1718431647865503744

Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms

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