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Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan †

This paper presents a global monocular indoor positioning system for a robotic vehicle starting from a known pose. The proposed system does not depend on a dense 3D map, require prior environment exploration or installation, or rely on the scene remaining the same, photometrically or geometrically....

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Detalles Bibliográficos
Autores principales: Noonan, John, Rotstein, Hector, Geva, Amir, Rivlin, Ehud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387000/
https://www.ncbi.nlm.nih.gov/pubmed/30717361
http://dx.doi.org/10.3390/s19030634
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author Noonan, John
Rotstein, Hector
Geva, Amir
Rivlin, Ehud
author_facet Noonan, John
Rotstein, Hector
Geva, Amir
Rivlin, Ehud
author_sort Noonan, John
collection PubMed
description This paper presents a global monocular indoor positioning system for a robotic vehicle starting from a known pose. The proposed system does not depend on a dense 3D map, require prior environment exploration or installation, or rely on the scene remaining the same, photometrically or geometrically. The approach presents a new way of providing global positioning relying on the sparse knowledge of the building floorplan by utilizing special algorithms to resolve the unknown scale through wall–plane association. This Wall Plane Fusion algorithm presented finds correspondences between walls of the floorplan and planar structures present in the 3D point cloud. In order to extract planes from point clouds that contain scale ambiguity, the Scale Invariant Planar RANSAC (SIPR) algorithm was developed. The best wall–plane correspondence is used as an external constraint to a custom Bundle Adjustment optimization which refines the motion estimation solution and enforces a global scale solution. A necessary condition is that only one wall needs to be in view. The feasibility of using the algorithms is tested with synthetic and real-world data; extensive testing is performed in an indoor simulation environment using the Unreal Engine and Microsoft Airsim. The system performs consistently across all three types of data. The tests presented in this paper show that the standard deviation of the error did not exceed 6 cm.
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spelling pubmed-63870002019-02-26 Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan † Noonan, John Rotstein, Hector Geva, Amir Rivlin, Ehud Sensors (Basel) Article This paper presents a global monocular indoor positioning system for a robotic vehicle starting from a known pose. The proposed system does not depend on a dense 3D map, require prior environment exploration or installation, or rely on the scene remaining the same, photometrically or geometrically. The approach presents a new way of providing global positioning relying on the sparse knowledge of the building floorplan by utilizing special algorithms to resolve the unknown scale through wall–plane association. This Wall Plane Fusion algorithm presented finds correspondences between walls of the floorplan and planar structures present in the 3D point cloud. In order to extract planes from point clouds that contain scale ambiguity, the Scale Invariant Planar RANSAC (SIPR) algorithm was developed. The best wall–plane correspondence is used as an external constraint to a custom Bundle Adjustment optimization which refines the motion estimation solution and enforces a global scale solution. A necessary condition is that only one wall needs to be in view. The feasibility of using the algorithms is tested with synthetic and real-world data; extensive testing is performed in an indoor simulation environment using the Unreal Engine and Microsoft Airsim. The system performs consistently across all three types of data. The tests presented in this paper show that the standard deviation of the error did not exceed 6 cm. MDPI 2019-02-02 /pmc/articles/PMC6387000/ /pubmed/30717361 http://dx.doi.org/10.3390/s19030634 Text en © 2019 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
Noonan, John
Rotstein, Hector
Geva, Amir
Rivlin, Ehud
Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan †
title Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan †
title_full Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan †
title_fullStr Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan †
title_full_unstemmed Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan †
title_short Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan †
title_sort global monocular indoor positioning of a robotic vehicle with a floorplan †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387000/
https://www.ncbi.nlm.nih.gov/pubmed/30717361
http://dx.doi.org/10.3390/s19030634
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