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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...
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/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. |
format | Online Article Text |
id | pubmed-6111593 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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