VeREFINE: Integrating Object Pose Verification with Physics-Guided Iterative Refinement

Bauer, D; Patten, T; Vincze, M

VeREFINE: Integrating Object Pose Verification with Physics-Guided Iterative Refinement

Bauer, D Patten, T Vincze, M

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2020, 5, (3), pp. 4289-4296
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Bauer, D
dc.contributor.author	Patten, T
dc.contributor.author	Vincze, M
dc.date.accessioned	2021-03-01T06:15:47Z
dc.date.available	2021-03-01T06:15:47Z
dc.date.issued	2020-07-01
dc.identifier.citation	IEEE Robotics and Automation Letters, 2020, 5, (3), pp. 4289-4296
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3766
dc.identifier.uri	http://hdl.handle.net/10453/146575
dc.description.abstract	© 2016 IEEE. Accurate and robust object pose estimation for robotics applications requires verification and refinement steps. In this work, we propose to integrate hypotheses verification with object pose refinement guided by physics simulation. This allows the physical plausibility of individual object pose estimates and the stability of the estimated scene to be considered in a unified optimization. The proposed method is able to adapt to scenes of multiple objects and efficiently focuses on refining the most promising object poses in multi-hypotheses scenarios. We call this integrated approach VeREFINE and evaluate it on three datasets with varying scene complexity. The generality of the approach is shown by using three state-of-the-art pose estimators and three baseline refiners. Results show improvements over all baselines and on all datasets. Furthermore, our approach is applied in real-world grasping experiments and outperforms competing methods in terms of grasp success rate. Code is publicly available at github.com/dornik/verefine.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Robotics and Automation Letters
dc.relation.isbasedon	10.1109/LRA.2020.2996059
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	0913 Mechanical Engineering
dc.title	VeREFINE: Integrating Object Pose Verification with Physics-Guided Iterative Refinement
dc.type	Journal Article
utslib.citation.volume	5
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2022-07-01T00:00:00+1000Z
dc.date.updated	2021-03-01T06:15:38Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	5
utslib.citation.issue	3

Abstract:

© 2016 IEEE. Accurate and robust object pose estimation for robotics applications requires verification and refinement steps. In this work, we propose to integrate hypotheses verification with object pose refinement guided by physics simulation. This allows the physical plausibility of individual object pose estimates and the stability of the estimated scene to be considered in a unified optimization. The proposed method is able to adapt to scenes of multiple objects and efficiently focuses on refining the most promising object poses in multi-hypotheses scenarios. We call this integrated approach VeREFINE and evaluate it on three datasets with varying scene complexity. The generality of the approach is shown by using three state-of-the-art pose estimators and three baseline refiners. Results show improvements over all baselines and on all datasets. Furthermore, our approach is applied in real-world grasping experiments and outperforms competing methods in terms of grasp success rate. Code is publicly available at github.com/dornik/verefine.

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http://hdl.handle.net/10453/146575