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Online Aerial Terrain Mapping for Ground Robot Navigation
This work presents a collaborative unmanned aerial and ground vehicle system which utilizes the aerial vehicle’s overhead view to inform the ground vehicle’s path planning in real time. The aerial vehicle acquires imagery which is assembled into a orthomosaic and then classified. These terrain class...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855888/ https://www.ncbi.nlm.nih.gov/pubmed/29461496 http://dx.doi.org/10.3390/s18020630 |
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author | Peterson, John Chaudhry, Haseeb Abdelatty, Karim Bird, John Kochersberger, Kevin |
author_facet | Peterson, John Chaudhry, Haseeb Abdelatty, Karim Bird, John Kochersberger, Kevin |
author_sort | Peterson, John |
collection | PubMed |
description | This work presents a collaborative unmanned aerial and ground vehicle system which utilizes the aerial vehicle’s overhead view to inform the ground vehicle’s path planning in real time. The aerial vehicle acquires imagery which is assembled into a orthomosaic and then classified. These terrain classes are used to estimate relative navigation costs for the ground vehicle so energy-efficient paths may be generated and then executed. The two vehicles are registered in a common coordinate frame using a real-time kinematic global positioning system (RTK GPS) and all image processing is performed onboard the unmanned aerial vehicle, which minimizes the data exchanged between the vehicles. This paper describes the architecture of the system and quantifies the registration errors between the vehicles. |
format | Online Article Text |
id | pubmed-5855888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-58558882018-03-20 Online Aerial Terrain Mapping for Ground Robot Navigation Peterson, John Chaudhry, Haseeb Abdelatty, Karim Bird, John Kochersberger, Kevin Sensors (Basel) Article This work presents a collaborative unmanned aerial and ground vehicle system which utilizes the aerial vehicle’s overhead view to inform the ground vehicle’s path planning in real time. The aerial vehicle acquires imagery which is assembled into a orthomosaic and then classified. These terrain classes are used to estimate relative navigation costs for the ground vehicle so energy-efficient paths may be generated and then executed. The two vehicles are registered in a common coordinate frame using a real-time kinematic global positioning system (RTK GPS) and all image processing is performed onboard the unmanned aerial vehicle, which minimizes the data exchanged between the vehicles. This paper describes the architecture of the system and quantifies the registration errors between the vehicles. MDPI 2018-02-20 /pmc/articles/PMC5855888/ /pubmed/29461496 http://dx.doi.org/10.3390/s18020630 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Peterson, John Chaudhry, Haseeb Abdelatty, Karim Bird, John Kochersberger, Kevin Online Aerial Terrain Mapping for Ground Robot Navigation |
title | Online Aerial Terrain Mapping for Ground Robot Navigation |
title_full | Online Aerial Terrain Mapping for Ground Robot Navigation |
title_fullStr | Online Aerial Terrain Mapping for Ground Robot Navigation |
title_full_unstemmed | Online Aerial Terrain Mapping for Ground Robot Navigation |
title_short | Online Aerial Terrain Mapping for Ground Robot Navigation |
title_sort | online aerial terrain mapping for ground robot navigation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855888/ https://www.ncbi.nlm.nih.gov/pubmed/29461496 http://dx.doi.org/10.3390/s18020630 |
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