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A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data

Pose estimation of free-form objects is a crucial task towards flexible and reliable highly complex autonomous systems. Recently, methods based on range and RGB-D data have shown promising results with relatively high recognition rates and fast running times. On this line, this paper presents a feat...

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Detalles Bibliográficos
Autores principales: Vidal, Joel, Lin, Chyi-Yeu, Lladó, Xavier, Martí, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111593/
https://www.ncbi.nlm.nih.gov/pubmed/30111697
http://dx.doi.org/10.3390/s18082678
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author Vidal, Joel
Lin, Chyi-Yeu
Lladó, Xavier
Martí, Robert
author_facet Vidal, Joel
Lin, Chyi-Yeu
Lladó, Xavier
Martí, Robert
author_sort Vidal, Joel
collection PubMed
description Pose estimation of free-form objects is a crucial task towards flexible and reliable highly complex autonomous systems. Recently, methods based on range and RGB-D data have shown promising results with relatively high recognition rates and fast running times. On this line, this paper presents a feature-based method for 6D pose estimation of rigid objects based on the Point Pair Features voting approach. The presented solution combines a novel preprocessing step, which takes into consideration the discriminative value of surface information, with an improved matching method for Point Pair Features. In addition, an improved clustering step and a novel view-dependent re-scoring process are proposed alongside two scene consistency verification steps. The proposed method performance is evaluated against 15 state-of-the-art solutions on a set of extensive and variate publicly available datasets with real-world scenarios under clutter and occlusion. The presented results show that the proposed method outperforms all tested state-of-the-art methods for all datasets with an overall 6.6% relative improvement compared to the second best method.
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spelling pubmed-61115932018-08-30 A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data Vidal, Joel Lin, Chyi-Yeu Lladó, Xavier Martí, Robert Sensors (Basel) Article Pose estimation of free-form objects is a crucial task towards flexible and reliable highly complex autonomous systems. Recently, methods based on range and RGB-D data have shown promising results with relatively high recognition rates and fast running times. On this line, this paper presents a feature-based method for 6D pose estimation of rigid objects based on the Point Pair Features voting approach. The presented solution combines a novel preprocessing step, which takes into consideration the discriminative value of surface information, with an improved matching method for Point Pair Features. In addition, an improved clustering step and a novel view-dependent re-scoring process are proposed alongside two scene consistency verification steps. The proposed method performance is evaluated against 15 state-of-the-art solutions on a set of extensive and variate publicly available datasets with real-world scenarios under clutter and occlusion. The presented results show that the proposed method outperforms all tested state-of-the-art methods for all datasets with an overall 6.6% relative improvement compared to the second best method. MDPI 2018-08-15 /pmc/articles/PMC6111593/ /pubmed/30111697 http://dx.doi.org/10.3390/s18082678 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
Vidal, Joel
Lin, Chyi-Yeu
Lladó, Xavier
Martí, Robert
A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data
title A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data
title_full A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data
title_fullStr A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data
title_full_unstemmed A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data
title_short A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data
title_sort method for 6d pose estimation of free-form rigid objects using point pair features on range data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111593/
https://www.ncbi.nlm.nih.gov/pubmed/30111697
http://dx.doi.org/10.3390/s18082678
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