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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
Descripción
Sumario: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.