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Multi-view Orientation Estimation using Bingham Mixture Models

Riedel, Sebastian and Marton, Zoltan Csaba and Kriegel, Simon (2016) Multi-view Orientation Estimation using Bingham Mixture Models. In: 2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2016). IEEE International Conference on Automation, Quality and Testing, Robotics. AQTR 2016, 19-21 May 2016, Cluj-Napoca, Romania.

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Abstract

This paper describes a multi-view pose estimation system, that is exploiting the mobility of a depth sensor through mounting it onto a robotic manipulator. Given a pose estimation algorithm that performs feature extraction and matching to a model database, we investigate the probabilistic modeling of the pose space as well as the measurement uncertainty, to be used in a sequential state estimation approach. Uncertainties in 3d position can be modeled in a parametric way by 3d Gaussians, but the space of rotations in 3d - the special orthogonal group SO(3) - requires approaches from directional statistics. A convenient representation for orientations are unit quaternions over which the Bingham distribution defines a parametric probability density function. The Bingham distribution also correctly accounts for the sign symmetry of orientation quaternions and leave degrees of freedom unconstrained (which is especially useful if an object is rotationally symmetric, with no unique quaternion describing its orientation). In our experiments we test different sequential fusion methods, optimize their parameters, and investigate how the derived filter performs in a case with high uncertainties.

Item URL in elib:https://elib.dlr.de/104238/
Document Type:Conference or Workshop Item (Speech)
Additional Information:The file, which is provided here, is a draft version. Please refer to www.ieeexplore.com for the final version of the article.
Title:Multi-view Orientation Estimation using Bingham Mixture Models
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Riedel, SebastianSebastian.Riedel (at) dlr.dehttps://orcid.org/0000-0002-3655-2486
Marton, Zoltan Csabazoltan.marton (at) dlr.deUNSPECIFIED
Kriegel, SimonSimon.Kriegel (at) dlr.dehttps://orcid.org/0000-0003-4711-8527
Date:2016
Journal or Publication Title:2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2016)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Series Name:Proceedings of IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)
Status:Published
Keywords:Rotational Uncertainty, Probabilistic Modeling, Mixture Models, Sequential Estimation
Event Title:IEEE International Conference on Automation, Quality and Testing, Robotics. AQTR 2016
Event Location:Cluj-Napoca, Romania
Event Type:international Conference
Event Dates:19-21 May 2016
Organizer:IEEE Computer Society - Test Technology Technical Council
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Riedel, Sebastian
Deposited On:18 Jul 2016 14:14
Last Modified:31 Jul 2019 20:01

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