Xu, Z., Papallas, R. and Dogar, M. orcid.org/0000-0002-6896-5461 (2024) Physics-Based Object 6D-Pose Estimation during Non-Prehensile Manipulation. In: Experimental Robotics. 18th International Symposium on Experimental Robotics, 26-30 Nov 2023, Chiang Mai, Thailand. Springer Proceedings in Advanced Robotics, 30 . Springer , Cham, Switzerland , pp. 181-191. ISBN 978-3-031-63595-3
Abstract
We propose a method to track the 6D pose of an object over time, while the object is under non-prehensile manipulation by a robot. At any given time during the manipulation of the object, we assume access to the robot joint controls and an image from a camera. We use the robot joint controls to perform a physics-based prediction of how the object might be moving. We then combine this prediction with the observation coming from the camera, to estimate the object pose as accurately as possible. We use a particle filtering approach to combine the control information with the visual information. We compare the proposed method with two baselines: (i) using only an image-based pose estimation system at each time-step, and (ii) a particle filter which does not perform the computationally expensive physics predictions, but assumes the object moves with constant velocity. Our results show that making physics-based predictions is worth the computational cost, resulting in more accurate tracking, and estimating object pose even when the object is not clearly visible to the camera.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of a conference paper accepted for publication in Springer Proceedings in Advanced Robotics, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Non-prehensile Manipulation, Object-Pose Estimation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/V052659/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Oct 2023 08:45 |
Last Modified: | 16 Aug 2024 13:05 |
Published Version: | https://link.springer.com/chapter/10.1007/978-3-03... |
Status: | Published |
Publisher: | Springer |
Series Name: | Springer Proceedings in Advanced Robotics |
Identification Number: | 10.1007/978-3-031-63596-0_16 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204438 |
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