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Category-based Object PoseEstimation with Pose OrientedSEmi-global Feature Histograms
Örebro University, School of Science and Technology.
2012 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis we propose a novel, fast and accurate 3D descriptor for use in

pose estimation of objects. To achieve this, we develop algorithm to identify

the pose of any instance of a particular object category without having its exact

model. Our aim is to estimate the object pose given only information about

its category. To achieve this, we focus on the impact of the choice of the reference

frame on the performance of a 3D descriptor. We propose a method that

estimates a reference coordinate system using a single viewpoint of the object

scene. We use an additional reference vector in order to define our reference

coordinate system uniquely. The main idea is to use the information retrieved

from a perception system collecting real world data. This information is neglected

in most of the state-of-the-art pose estimation methods. In this work,

we use the information which is given by the support plane that the objects

are lying on. Based on the reference coordinate system, we define the Pose Oriented

SEmi-global Feature Histogram (POSEFH) as an efficient 3D semi-global

descriptor which encodes the geometrical properties of the object surface. We

perform extensive qualitative and quantitative evaluation of our method on the

subset of the RGB-D Stereo Object Category database which is collected by the

7-joint Armar III robotic head with foveal and peripheral stereo cameras We

compare it with the state-of-the-art 3D descriptors, the Viewpoint Featrue Histogram

(VFH) and the Clustered Viewpoint Feature Histogram (CVFH). Our

method works for incomplete 3D pointclouds of objects. The results show a

significant improvement in the accuracy of one degree-of-freedom orientation

estimation while dealing with partial and real noisy data.

i

Place, publisher, year, edition, pages
2012. , p. 79
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Identifiers
URN: urn:nbn:se:oru:diva-32130OAI: oai:DiVA.org:oru-32130DiVA, id: diva2:659396
Subject / course
Computer Engineering
Supervisors
Examiners
Available from: 2013-10-25 Created: 2013-10-25 Last updated: 2017-10-17Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf